Neural Network & Dialog Systems 2016


Neural Dialog Systems (Neural Dialogue Systems)

Notes:

A statistical language model is a probability distribution over sequences of words.

  • Neural conversation model
  • Neural network language model

Resources:

  • ijcnn.org .. international joint conference on neural networks
  • opennn.cimne.com .. multilayer perceptron neural network in  C++

Wikipedia:

References:

See also:

Classification Algorithms In Dialog Systems | Classifiers In Dialog Systems | Holographic Neural Technology | Ontology In Dialog SystemsParsing Algorithms & Dialog Systems


This table represents the relative importance of neural network topics to dialog systems, based on the number of academic publications returned for the period 2012 to 2017. Topics with zero significance have been removed.

Perceptron 472
Recurrent neural network 461
Long short-term memory 365
Activation function 247
Word embedding 236
Backpropagation 213
Radial basis function 200
Sigmoid function 196
Multilayer perceptron 195
Word2vec 191
Convolutional neural network 180
Dropout (neural networks) 179
Feedforward (control) 124
Softmax function 123
Autoencoder 105
Early stopping 91
Multimodal learning 63
Vanishing gradient problem 55
Deep belief network 54
Feedforward neural network 53
Backpropagation through time 49
Recursive neural network 45
Boltzmann machine 42
Self-organizing map 41
Probabilistic neural network 40
Restricted Boltzmann machine 37
Learning rule 33
Learning vector quantization 31
Bidirectional recurrent neural networks 29
Artificial Intelligence System 25
Time delay neural network 20
Competitive learning 19
Rprop 19
Artificial neuron 17
Rectifier (neural networks) 17
Adaptive neuro fuzzy inference system 15
Radial basis function network 15
Caffe (software) 14
Neural Networks (journal) 13
Adaptive resonance theory 12
Neural gas 12
Spiking neural network 11
Winner-take-all (computing) 11
Reservoir computing 9
CoDi 8
Computational cybernetics 8
Convolutional Deep Belief Networks 8
Modular neural network 8
Delta rule 7
Echo state network 7
The Emotion Machine 7
Committee machine 6
Hierarchical temporal memory 6
Infomax 6
Quickprop 6
Deeplearning4j 5
Hybrid neural network 5
Liquid state machine 5
Synaptic weight 5
European Neural Network Society 4
Neocognitron 4
Catastrophic interference 3
Generalized Hebbian Algorithm 3
Hopfield network 3
U-matrix 3
ADALINE 2
Bidirectional associative memory 2
Counter propagation network 2
Leabra 2
Linde–Buzo–Gray algorithm 2
NETtalk (artificial neural network) 2
Randomneural network 2
Universal approximation theorem 2
Cellular neural network 1
Compositional pattern-producing network 1
Helmholtz machine 1
MoneyBee 1
Neuroevolution of augmenting topologies 1
Physical neural network 1

A network-based end-to-end trainable task-oriented dialogue system
TH Wen, D Vandyke, N Mrksic, M Gasic… – arXiv preprint arXiv: …, 2016 – arxiv.org
… This paper has presented a novel neural network-based framework for task-oriented dialogue systems. … To the best of our knowledge, this is the first end-to-end neural network-based dialogue system that can conduct meaningful dialogues in a task-oriented application. …

Multi-domain neural network language generation for spoken dialogue systems
TH Wen, M Gasic, N Mrksic… – arXiv preprint arXiv: …, 2016 – arxiv.org
Abstract: Moving from limited-domain natural language generation (NLG) to open domain is difficult because the number of semantic input combinations grows exponentially with the number of domains. Therefore, it is important to leverage existing resources and exploit

How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation
CW Liu, R Lowe, IV Serban, M Noseworthy… – arXiv preprint arXiv: …, 2016 – arxiv.org
… The model is trained using negative sampling to minimize the cross-entropy error of all (context, response) pairs. To our knowledge, our applica- tion of neural network models to large-scale re- trieval in dialogue systems is novel. …

Sequential short-text classification with recurrent and convolutional neural networks
JY Lee, F Dernoncourt – arXiv preprint arXiv:1603.03827, 2016 – arxiv.org
… 2014. A convolu- tional neural network for modelling sentences. arXiv preprint arXiv:1404.2188. … 2016. The Fourth Dialog State Tracking Chal- lenge. In Proceedings of the 7th International Work- shop on Spoken Dialogue Systems (IWSDS). [Kim2014] Yoon Kim. 2014. …

A latent variable recurrent neural network for discourse relation language models
Y Ji, G Haffari, J Eisenstein – arXiv preprint arXiv:1603.01913, 2016 – arxiv.org
… the latent vari- ables, our model is simple to implement and train, requiring only minimal modifications to existing re- current neural network architectures that … in the Switchboard cor- pus (Stolcke et al., 2000), and is a key component of contemporary dialog systems (Williams and …

A persona-based neural conversation model
J Li, M Galley, C Brockett, GP Spithourakis… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Since generating meaningful re- sponses in an open-domain scenario is intrinsi- cally difficult in conventional dialog systems, ex- isting models often focus on generalizing character style on the basis of qualitative statistical analysis (Walker et al., 2012; Walker et al., 2011). …

Learning to respond with deep neural networks for retrieval-based human-computer conversation system
R Yan, Y Song, H Wu – Proceedings of the 39th International ACM SIGIR …, 2016 – dl.acm.org
Page 1. Learning to Respond with Deep Neural Networks for … In this paper, we propose a retrieval-based conversation system with the deep learning-to- respond schema through a deep neural network framework driven by web data. …

Video paragraph captioning using hierarchical recurrent neural networks
H Yu, J Wang, Z Huang, Y Yang… – Proceedings of the IEEE …, 2016 – cv-foundation.org
Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks … We present an approach that exploits hierarchical Recur- rent Neural Networks (RNNs) to tackle the video captioning problem, ie, generating one or multiple sentences to de- scribe a realistic video. …

End-to-end lstm-based dialog control optimized with supervised and reinforcement learning
JD Williams, G Zweig – arXiv preprint arXiv:1606.01269, 2016 – arxiv.org
… 7 Conclusion This paper has taken a first step toward end-to- end learning of task-oriented dialog systems. Our approach is based on a recurrent neural network which maps from raw dialog history to distribu- tions over actions. …

Counter-fitting word vectors to linguistic constraints
N Mrkši?, DO Séaghdha, B Thomson, M Gaši?… – arXiv preprint arXiv: …, 2016 – arxiv.org
… on exploiting antonymy in dialogue systems. The modelling work closest to ours are Liu et al. (2015), who use antonymy and WordNet hierarchy information to modify the heavyweight Word2Vec training objective; Yih et al. (2012), who use a Siamese neural network to improve …

Recurrent neural network grammars
C Dyer, A Kuncoro, M Ballesteros, NA Smith – arXiv preprint arXiv: …, 2016 – arxiv.org
Page 1. Recurrent Neural Network Grammars Chris Dyer? Adhiguna Kuncoro? Miguel Ballesteros?? Noah A. Smith? … Abstract We introduce recurrent neural network gram- mars, probabilistic models of sentences with explicit phrase structure. …

Deep reinforcement learning for dialogue generation
J Li, W Monroe, A Ritter, M Galley, J Gao… – arXiv preprint arXiv: …, 2016 – arxiv.org
… To achieve these goals, we draw on the insights of reinforcement learning, which have been widely ap- plied in MDP and POMDP dialogue systems (see Re- lated Work section for details). … 2 Related Work Efforts to build statistical dialog systems fall into two major categories. …

Learning end-to-end goal-oriented dialog
A Bordes, J Weston – arXiv preprint arXiv:1605.07683, 2016 – arxiv.org
… Processing. Serban, IV, Sordoni, A., Bengio, Y., Courville, A., and Pineau, J. (2015a). Building end-to-end dialogue systems using generative hierarchical neural network models. In Proc. of the AAAI Conference on Artificial Intelligence. …

Introduction
F Eyben – Real-time Speech and Music Classification by Large …, 2016 – Springer
… scale, efficient off-line batch processing for research, as well as for on-line, incremental processing in interactive speech dialogue systems, for example. … 5(9/10), 341–345 (2001). G. Dahl, D. Yu, L. Deng, A. Acero, Context-dependent pre-trained deep neural networks for large …

Dialog-based language learning
JE Weston – Advances in Neural Information Processing Systems, 2016 – papers.nips.cc
Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/).

Contextual LSTM (CLSTM) models for Large scale NLP tasks
S Ghosh, O Vinyals, B Strope, S Roy, T Dean… – arXiv preprint arXiv: …, 2016 – arxiv.org
… In this paper, we present CLSTM (Con- textual LSTM), an extension of the recurrent neural network LSTM (Long-Short Term … a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems. …

Conditional generation and snapshot learning in neural dialogue systems
TH Wen, M Gasic, N Mrksic… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 3 Neural Dialogue System The testbed for this work is a neural network-based task-oriented dialogue system proposed by Wen et al. (2016a). The model casts dialogue as a source to target sequence transduction problem (modelled …

Key-value memory networks for directly reading documents
A Miller, A Fisch, J Dodge, AH Karimi, A Bordes… – arXiv preprint arXiv: …, 2016 – arxiv.org
… algorithms. In this work we propose the Key-Value Memory Network (KV-MemNN), a new neural network architecture that generalizes the original Memory Network (Sukhbaatar et al., 2015) and can work with either knowledge source. …

End-to-end memory networks with knowledge carryover for multi-turn spoken language understanding
YN Chen, D Hakkani-Tür, G Tur, J Gao… – Proceedings of …, 2016 – microsoft.com
… Spoken language understanding (SLU) is a core component of a spoken dialogue system. … ex- tract salient semantics for modeling knowledge carryover in the multi-turn conversations and outperform the results using the state-of-the-art recurrent neural network framework (RNN …

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues
IV Serban, A Sordoni, R Lowe, L Charlin… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 9 Page 10. [24] Serban, IV, Sordoni, A., Bengio, Y., Courville, AC, and Pineau, J. (2016). Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI, pages 3776–3784. [25] Shaikh, S., Strzalkowski, T., Taylor, S., and Webb, N. (2010). …

Policy networks with two-stage training for dialogue systems
M Fatemi, LE Asri, H Schulz, J He… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Therefore, we conclude that TDA2C is very appealing for the practical deployment of POMDP-based dialogue systems. … which maps a belief state Bt to the values of the possible actions At ? A(Bt = b) at that state, Q?(Bt,At; wt), where wt is the weight vector of the neural network. …

Multiresolution Recurrent Neural Networks: An Application to Dialogue Response Generation
IV Serban, T Klinger, G Tesauro… – arXiv preprint arXiv: …, 2016 – arxiv.org
… CoRR, abs/1512.05742. [28] Serban, IV, Sordoni, A., Bengio, Y., Courville, AC, and Pineau, J. (2016a). Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI, pages 3776–3784. …

Learning distributed representations of sentences from unlabelled data
F Hill, K Cho, A Korhonen – arXiv preprint arXiv:1602.03483, 2016 – arxiv.org
… Examples include machine translation (Sutskever et al., 2014), image captioning (Mao et al., 2015) and dialogue systems (Serban et al., 2015). … As with all sequence-to-sequence models, in train- ing the source sentence is ‘encoded’ by a Recurrent Neural Network (RNN) (with …

Continuously learning neural dialogue management
PH Su, M Gasic, N Mrksic, L Rojas-Barahona… – arXiv preprint arXiv: …, 2016 – arxiv.org
… We describe a two-step approach for dialogue management in task-oriented spoken dialogue systems. A unified neural network framework is proposed to enable the system to first learn by supervision from a set of dialogue data and then continuously improve its behaviour via …

Mutual information and diverse decoding improve neural machine translation
J Li, D Jurafsky – arXiv preprint arXiv:1601.00372, 2016 – arxiv.org
Page 1. Mutual Information and Diverse Decoding Improve Neural Machine Translation Jiwei Li and Dan Jurafsky Computer Science Department Stanford University, Stanford, CA, 94305, USA jiweil,jurafsky@stanford.edu Abstract …

Towards end-to-end learning for dialog state tracking and management using deep reinforcement learning
T Zhao, M Eskenazi – arXiv preprint arXiv:1606.02560, 2016 – arxiv.org
… users. The typical structure of a task-oriented dialog system is outlined in Fig- ure 1 (Young, 2006). This … query. This is difficult for conventional neural network models which do not provide intermediate symbolic representations. This …

Sequence-to-sequence learning as beam-search optimization
S Wiseman, AM Rush – arXiv preprint arXiv:1606.02960, 2016 – arxiv.org
… Sequence-to-Sequence learning with deep neural networks (herein, seq2seq) (Sutskever et al., 2011; Sutskever et al., 2014) has rapidly become a … to be useful for sen- tence compression (Filippova et al., 2015), parsing (Vinyals et al., 2015), and dialogue systems (Ser- ban et al …

Question detection from acoustic features using recurrent neural network with gated recurrent unit
Y Tang, Y Huang, Z Wu, H Meng… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… Second, in most spoken dialog systems, automatic speech recognition (ASR) is the foremost step whose performance will have huge impacts on … Recurrent neural networks (RNN) can model context information along time steps of sequence [9]. Inspired by this characteristic, we …

Dialogue State Tracking using Long Short Term Memory Neural Networks
K Yoshino, T Hiraoka, G Neubig, S Nakamura – 2016 – colips.org
… This change expands the variety of expressions of users, because the users will be free from limitations im- posed by dialogue systems. … Previously, recurrent neural networks (RNNs) have been used for the dialogue state tracking [2, 1] and they achieved good results in …

Multi-domain joint semantic frame parsing using bi-directional RNN-LSTM
D Hakkani-Tür, G Tur, A Celikyilmaz… – Proceedings of The …, 2016 – csie.ntu.edu.tw
… L. Deng, D. Hakkani- Tur, X. He, L. Heck, G. Tur, D. Yu, and G. Zweig, “Using re- current neural networks for slot … N. Mrksic, P.-H. Su, D. Vandyke, and S. Young, “Semantically conditioned LSTM-based natural lan- guage generation for spoken dialogue systems,” arXiv preprint …

Overview of the NTCIR-12 short text conversation task
L Shang, T Sakai, Z Lu, H Li… – … of NTCIR-12, 2016 – research.nii.ac.jp
… We review in this paper the task definition, evaluation measures, test collections, and the evaluation results of all teams. Keywords artificial intelligence, dialogue systems, evaluation, information re- trieval, natural language processing, social media, test collections. …

Stalematebreaker: A proactive content-introducing approach to automatic human-computer conversation
X Li, L Mou, R Yan, M Zhang – arXiv preprint arXiv:1604.04358, 2016 – arxiv.org
… 2.1 Dialogue systems • Domain-specific systems. … Higashinaka et al., 2014; Ji et al., 2014]. Generative methods—typically using sta- tistical machine translation techinques [Ritter et al., 2011; Sugiyama et al., 2013; Mairesse and Young, 2014] or neural networks [Shang et al …

Speaker adaptation of hybrid NN/HMM model for speech recognition based on singular value decomposition
S Xue, H Jiang, L Dai, Q Liu – Journal of Signal Processing Systems, 2016 – Springer
… Abstract Recently several speaker adaptation methods have been proposed for deep neural network (DNN) in many large vocabulary continuous speech recognition (LVCSR) tasks. … Recently, a number of speaker adaptation methods have been proposed for neural networks. …

Deep recurrent models with fast-forward connections for neural machine translation
J Zhou, Y Cao, X Wang, P Li, W Xu – arXiv preprint arXiv:1606.04199, 2016 – arxiv.org
… Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent … Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question …

The dialog state tracking challenge series: A review
J Williams, A Raux, M Henderson – Dialogue & Discourse, 2016 – dad.uni-bielefeld.de
… 7 Page 5. WILLIAMS, RAUX, AND HENDERSON 3.1 Hand-crafted rules for dialog state tracking Early spoken dialog systems used hand-crafted rules for dialog state tracking. … Henderson et al. (2013) applies a deep neural network as a classifier. …

Transfer learning for user adaptation in spoken dialogue systems
A Genevay, R Laroche – … of the 2016 International Conference on …, 2016 – dl.acm.org
… com ABSTRACT This paper focuses on user adaptation in Spoken Dialogue Systems. It … Systems 1. INTRODUCTION Spoken dialogue systems have the ability to interact di- rectly with a human through speech. Reinforcement …

Implicit distortion and fertility models for attention-based encoder-decoder NMT model
S Feng, S Liu, M Li, M Zhou – arXiv preprint arXiv:1601.03317, 2016 – arxiv.org
Page 1. Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model Shi Feng† Shanghai Jiao Tong University Shanghai, PR China sjtufs@gmail.com Shujie Liu, Mu Li, Ming Zhou Microsoft Research …

Sequence to backward and forward sequences: A content-introducing approach to generative short-text conversation
L Mou, Y Song, R Yan, G Li, L Zhang, Z Jin – arXiv preprint arXiv: …, 2016 – arxiv.org
… For neural network-based dialogue systems, Sordoni et al. (2015) summarize a query and context as bag-of-words features, based on which an RNN decodes the reply. … 2016a. Building end-to-end dialogue systems using generative hierarchical neural network models. …

“The dialogue breakdown detection challenge: Task description, datasets, and evaluation metrics”
R Higashinaka, K Funakoshi, K Yuka… – of the Language …, 2016 – lrec-conf.org
… Fatal or not? Finding errors that lead to dialogue breakdowns in chat-oriented dialogue systems. In Proc. … (in Japanese). Kobayashi, S., Unno, Y., and Fukuda, M. (2015). Multi- task learning of recurrent neural network for detecting breakdowns of dialog and language modeling. …

A neural knowledge language model
S Ahn, H Choi, T Pärnamaa, Y Bengio – arXiv preprint arXiv:1608.00318, 2016 – arxiv.org
… it has been an indispensable component in many language/speech tasks such as speech recognition [26, 1], machine translation [17], and dialogue systems [40, 34]. … There have been remarkable advances in language modeling research based on neural networks [4, 26]. …

Hierarchical Memory Networks
S Chandar, S Ahn, H Larochelle, P Vincent… – arXiv preprint arXiv: …, 2016 – arxiv.org
… respect to the final task at hand. However, simple encode-decode style neural networks often underperform on knowledge-based reasoning tasks like question-answering or dialog systems. Indeed, in such cases it is nearly …

Training an adaptive dialogue policy for interactive learning of visually grounded word meanings
Y Yu, A Eshghi, O Lemon – 17th Annual Meeting of the Special Interest …, 2016 – aclweb.org
… 2014. How domain- general can we be? learning incremental dialogue systems without dialogue acts. In Proceedings of SemDial. … An in- cremental network for on-line unsupervised classi- fication and topology learning. Neural Networks, 19 (1): 90–106. Jonathan Ginzburg. …

LSTM based Conversation Models
Y Luan, Y Ji, M Ostendorf – arXiv preprint arXiv:1603.09457, 2016 – arxiv.org
… Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. arXiv preprint arXiv:1508.01745, 2015. … [13] Rui Lin, Shujie Liu, Muyun Yang, Mu Li, Ming Zhou, and Sheng Li. Hierarchical recurrent neural network for document modeling. …

Regularizing RNNs by stabilizing activations
D Krueger, R Memisevic – Proceeding of the International …, 2016 – pdfs.semanticscholar.org
… language generation for spoken dialogue systems. CoRR, abs/1508.01745, 2015. URL http://arxiv.org/abs/1508.01745. 8 Page 9. Under review as a conference paper at ICLR 2016 Zaremba, Wojciech, Sutskever, Ilya, and Vinyals, Oriol. Recurrent neural network regularization. …

Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models
YN Chen, D Hakkani-Tür, X He – Acoustics, Speech and Signal …, 2016 – ieeexplore.ieee.org
… Finally the new intents can be included in the dialogue systems without the need of new, associated training sam- ples, and model training, reducing human effort and … The model is a deep neural network with the convolutional struc- ture, where the architecture is illustrated in Fig …

A neural network approach to intention modeling for user-adapted conversational agents
D Griol, Z Callejas – Computational intelligence and neuroscience, 2016 – dl.acm.org
… the task is one of the main innovations of the paper as it is used not only for the user simulator but also for the practical implementation of the dialogue system. Another important contribution is that such implementation is performed with a neural-network-based classifier trained …

Sequence-to-Sequence Generation for Spoken Dialogue via Deep Syntax Trees and Strings
O Dušek, F Jur?í?ek – arXiv preprint arXiv:1606.05491, 2016 – arxiv.org
… for complex information presen- tation in spoken dialog systems. In Proceedings of the 42nd Annual Meeting on Association for Com- putational Linguistics, pages 79–86. I. Sutskever, O. Vinyals, and Q. VV Le. 2014. Se- quence to sequence learning with neural networks. …

On-line active reward learning for policy optimisation in spoken dialogue systems
PH Su, M Gasic, N Mrksic, L Rojas-Barahona… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 5 Conclusion In this paper we have proposed an active reward learning model using Gaussian process classifica- tion and an unsupervised neural network-based di- alogue embedding to enable truly on-line policy learning in spoken dialogue systems. …

Efficient exploration for dialogue policy learning with BBQ networks & replay buffer spiking
ZC Lipton, J Gao, L Li, X Li, F Ahmed… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Our paper touches several areas of research, namely Bayesian neural networks, reinforcement learning with deep Q-networks, Thompson Sampling, and dialogue systems. This work employs Q-learning [Watkins and Dayan, 1992], a popular method for model-free RL. …

End-to-end reinforcement learning of dialogue agents for information access
B Dhingra, L Li, X Li, J Gao, YN Chen, F Ahmed… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Statistical goal-oriented dialogue systems have long been modeled as partially observable Markov deci- sion processes (POMDPs) (Young et al … in designing “end-to-end” systems, which combine feature extraction and policy optimization using deep neural networks, with the …

Polyglot neural language models: A case study in cross-lingual phonetic representation learning
Y Tsvetkov, S Sitaram, M Faruqui, G Lample… – arXiv preprint arXiv: …, 2016 – arxiv.org
… To overcome the notorious problem in recurrent neural networks of vanishing gradients (Bengio et al., 1994), following Sundermeyer et al. … Text-to-speech (TTS) sys- tems are also used as part of speech-to-speech trans- lation systems and spoken dialog systems, such as …

Neural belief tracker: Data-driven dialogue state tracking
N Mrkši?, DO Séaghdha, TH Wen, B Thomson… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Models for probabilistic dialogue state tracking, or belief tracking, were introduced as components of spoken dialogue systems in order to better handle … according to its role in the user’s intent; standard labelling models such as CRFs or Re- current Neural Networks can then be …

The SpeDial datasets: datasets for spoken dialogue system analytics
J Lopes, A Chorianopoulou, E Palogiannidi… – Proceedings of the …, 2016 – speech.kth.se
… Keywords: Spoken Dialogue Systems, Multi-lingual Data, Emotions, Sentiment Analysis … The SpeDial gender classification module used in these experiments is a modified version of the frame-level gen- der classifier based on artificial neural network modelling described in …

Strategy and policy learning for non-task-oriented conversational systems
Z Yu, Z Xu, AW Black, AI Rudnicky – 17th Annual Meeting of the Special …, 2016 – aclweb.org
… For example, a neural network generation system (Vinyals and Le, 2015) can use the posterior prob- ability to decide if the generated utterance is … In a stochastic envi- ronment, a dialog system’s actions are system ut- terances, and the state is represented by the dialog history. …

Incorporating Loose-Structured Knowledge into LSTM with Recall Gate for Conversation Modeling
Z Xu, B Liu, B Wang, C Sun, X Wang – arXiv preprint arXiv:1605.05110, 2016 – arxiv.org
… con- versation modeling architectures, this paper tries to explore the effect of the background knowledge to the Neural Network based models … users’ context-aware queries and further select best answers based on the conversation his- tory, for building automatic dialog systems. …

Analysis of Emotional Speech—A Review
P Gangamohan, SR Kadiri… – Toward Robotic Socially …, 2016 – Springer
… In: Affective dialogue systems. Springer, pp 13–24. 2. Airas M, Pulakka H, Bäckström T, Alku P (2005) A toolkit for voice inverse filtering and parametrization. … Hansen JH, Womack BD (1996) Feature analysis and neural network-based classification of speech under stress. …

Dialog History Construction with Long-Short Term Memory for Robust Generative Dialog State Tracking
BJ Lee, KE Kim – Dialogue & Discourse, 2016 – dad.uni-bielefeld.de
… is heavily affected by that of the dialog tracker, it has been one of the core areas of research on dialog systems. In this paper, we present a dialog state tracker that combines a generative probabilistic model of dialog state tracking with the recurrent neural network for encoding …

Recurrent Neural Networks for Dialogue State Tracking
O Plátek, P B?lohlávek, V Hude?ek… – arXiv preprint arXiv: …, 2016 – arxiv.org
… and compared two dialogue state tracking models which are based on state-of-the-art architectures using recurrent neural networks. … Informal experiments were conducted during the Statistical Dialogue Systems course at Charles University (see https://github.com/oplatek/sds …

Cuni system for wmt16 automatic post-editing and multimodal translation tasks
J Libovický, J Helcl, M Tlustý, P Pecina… – arXiv preprint arXiv: …, 2016 – arxiv.org
… [Sutskeveretal.2014] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. Sequence to sequence learning with neural networks. … 2015. Semantically conditioned lstm-based natural language generation for spoken dialogue systems. …

Dialog state tracking with attention-based sequence-to-sequence learning
T Hori, H Wang, C Hori, S Watanabe… – … (SLT), 2016 IEEE, 2016 – ieeexplore.ieee.org
… understanding (SLU) technology, which predicts the intention of spoken user utterances, is a key component of dialog systems [1, 2 … Support vec- tor machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) have been applied to utterance …

Knowledge as a teacher: Knowledge-guided structural attention networks
YN Chen, D Hakkani-Tur, G Tur, A Celikyilmaz… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Abstract Natural language understanding (NLU) is a core component of a spoken dialogue system. Recently recurrent neural networks (RNN) ob- tained strong results on NLU due to their supe- rior ability of preserving sequential informa- tion over time. …

Log-linear rnns: Towards recurrent neural networks with flexible prior knowledge
M Dymetman, C Xiao – arXiv preprint arXiv:1607.02467, 2016 – arxiv.org
… Abstract We introduce LL-RNNs (Log-Linear RNNs), an extension of Re- current Neural Networks that replaces the softmax output layer by a log-linear output layer, of which the softmax is a special case. This conceptually simple move has two main advantages. …

Neural utterance ranking model for conversational dialogue systems
M Inaba, K Takahashi – 17th Annual Meeting of the Special Interest …, 2016 – aclweb.org
… 6 Conclusions In this study, we proposed a new utterance selec- tion method called the NUR model for conversa- tional dialogue systems. Our model ranks candi- date utterances by their suitability in given con- texts using neural networks. …

Transcrater: a tool for automatic speech recognition quality estimation
S Jalalvand, M Negri, M Turchi, JGC de Souza… – Proceedings of ACL- …, 2016 – aclweb.org
… Often, in- deed, the nature of such applications (consider for instance spoken dialog systems) requires quick re- sponse capabilities that are … et al., 2015b; Jalalvand and Falavigna, 2015) we showed that, instead of only one LM, using a combination of neural network and n-gram …

Chatbot evaluation and database expansion via crowdsourcing
Z Yu, Z Xu, A Black, A Rudnicky – Proc. of the chatbot …, 2016 – pdfs.semanticscholar.org
… Unlike goal oriented dialog systems, chat- bots do not have any specific goal that guides the interac- tion. … re- sponses, such as machine translation (Ritter et al., 2011), retrieval based response selection (Banchs and Li, 2012), and recurrent neural network sequence generation …

Exploring convolutional and recurrent neural networks in sequential labelling for dialogue topic tracking
S Kim, RE Banchs, H Li – 54th Annual Meeting of the Association for …, 2016 – aclweb.org
… tracking, including convolutional neural networks to account for semantics at each individual utterance, and recurrent neural networks to account for … These human capabilities for handling topics are also expected from dialogue systems to achieve natural and human-like conver …

Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
R Al-Rfou, M Pickett, J Snaider, Y Sung… – arXiv preprint arXiv: …, 2016 – arxiv.org
… large scale. We use a scalable neural network architecture that is able to take advantage of the large data size. In Section 2, we discuss recent relevant work in data-driven modeling of dialogue systems. Section 3 discusses …

A comparative study of recurrent neural network models for lexical domain classification
S Ravuri, A Stolcke – Acoustics, Speech and Signal Processing …, 2016 – ieeexplore.ieee.org
… ABSTRACT Domain classification is a critical pre-processing step for many speech understanding and dialog systems, as it allows for certain types of utterances to be routed to specialized subsystems. In pre- vious work, we explored various neural network (NN) architectures …

Controlling output length in neural encoder-decoders
Y Kikuchi, G Neubig, R Sasano, H Takamura… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Recent work has adopted techniques such as encoder-decoder (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Cho et al., 2014) and atten- tional (Bahdanau et al., 2015; Luong et al., 2015) neural network models from the field of machine translation, and tailored …

Two are Better than One: An Ensemble of Retrieval-and Generation-Based Dialog Systems
Y Song, R Yan, X Li, D Zhao, M Zhang – arXiv preprint arXiv:1610.07149, 2016 – arxiv.org
… 2.3 The biseq2seq Utterance Generator Using neural networks to build end-to-end trainable dialog systems has become a new research trend in the past year. … Building end- to-end dialogue systems using generative hierarchical neural network models. …

Search challenges in natural language generation with complex optimization objectives
V Demberg, J Hoffmann, DM Howcroft, D Klakow… – KI-Künstliche …, 2016 – Springer
… concise utter- ances (which are often more complex) when the user can fully concentrate on the interaction with the dialog system [9]. Achieving … extracted from the chart, and are evaluated by a refined quality objective for the final ranking, such as a neural network trained with …

Sequence-level knowledge distillation
Y Kim, AM Rush – arXiv preprint arXiv:1606.07947, 2016 – arxiv.org
… ap- proaches. NMT systems directly model the proba- bility of the next word in the target sentence sim- ply by conditioning a recurrent neural network on the source sentence and previously generated target words. While both …

Sequence-based structured prediction for semantic parsing
C Xiao, M Dymetman, C Gardent – Proceedings Association For …, 2016 – aclweb.org
… Given the recently shown effectiveness of RNNs (Recurrent Neural Networks), in particu- lar Long Short Term Memory (LSTM) networks (Hochreiter and Schmidhuber, 1997), for perform- ing sequence prediction in NLP applications such as machine translation (Sutskever et al …

CFGs-2-NLU: Sequence-to-sequence learning for mapping utterances to semantics and pragmatics
AJ Summerville, J Ryan, M Mateas… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Keywords: natural language understanding · conversational agent · chatbot · machine learning · neural network · sequence-to-sequence · lstm · context-free … But while service dialogue systems have become common, general conversational agents are still an open area of …

A context-aware natural language generator for dialogue systems
O Dušek, F Jur?í?ek – arXiv preprint arXiv:1608.07076, 2016 – arxiv.org
… neural networks with convolutional sentence reranking. In Proc. of SIGDIAL, pages 275–284. T.-H. Wen, M. Gasic, N. Mrkšic, P.-H. Su, D. Vandyke, and S. Young. 2015b. Semantically conditioned LSTM-based natural language generation for spo- ken dialogue systems. In Proc. …

On the Evaluation of Dialogue Systems with Next Utterance Classification
R Lowe, IV Serban, M Noseworthy, L Charlin… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 2015. A survey of available corpora for build- ing data-driven dialogue systems. arXiv preprint arXiv:1512.05742. IV Serban, A. Sordoni, Y. Bengio, AC Courville, and J. Pineau. 2016. Building end-to-end dia- logue systems using generative hierarchical neural network models. …

A unified framework for translation and understanding allowing discriminative joint decoding for multilingual speech semantic interpretation
B Jabaian, F Lefèvre, L Besacier – Computer Speech & Language, 2016 – Elsevier
… The framework can be generalized to other components of a dialogue system. Abstract. … Keywords. Multilingual speech understanding; Conditional random fields; Hypothesis graphs; Statistical machine translation; Dialogue systems. 1. Introduction. …

A regression approach to single-channel speech separation via high-resolution deep neural networks
J Du, Y Tu, LR Dai, CH Lee – IEEE/ACM Transactions on Audio, Speech …, 2016 – dl.acm.org
… Separation Via High-Resolution Deep Neural Networks Jun Du, Yanhui Tu, Li-Rong Dai, and Chin-Hui Lee, Fellow, IEEE … Index Terms—Deep neural network, divide and conquer, dual outputs, robust speech recognition, speech separation. I. INTRODUCTION …

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling
B Liu, I Lane – arXiv preprint arXiv:1609.01454, 2016 – arxiv.org
… In this work, we propose an attention-based neural network model for joint intent detection and slot filling, both of which are critical steps for many speech understanding and dialog systems. Unlike in machine translation and speech recognition, alignment is explicit in slot filling. …

Assisting discussion forum users using deep recurrent neural networks
JHP Suorra, O Mogren – Proceedings of the 1st Workshop on …, 2016 – aclweb.org
… Joelle Pineau. 2016. Building end-to-end dialogue systems using gener- ative hierarchical neural network models. In Dale Schuurmans and Michael P. Wellman, editors, AAAI, pages 3776–3784. AAAI Press. Alessandro Sordoni …

A sequence-to-sequence model for user simulation in spoken dialogue systems
LE Asri, J He, K Suleman – arXiv preprint arXiv:1607.00070, 2016 – arxiv.org
… J. Schatzmann, B. Thomson, K. Weilhammer, H. Ye, and S. Young, “Agenda-based user simulation for bootstrapping a POMDP dialogue system,” in Proc. of HLT, 2007. [17] I. Sutskever, O. Vinyals, and QV Le, “Sequence to sequence learning with neural networks,” CoRR, 2014 …

Erica: The erato intelligent conversational android
DF Glas, T Minato, CT Ishi, T Kawahara… – Robot and Human …, 2016 – ieeexplore.ieee.org
… This will be realized by using deep neural networks (DNN) for front-end speech enhancement and acoustic modeling [22 … We would like to thank Jani Even, Florent Ferreri, Koji Inoue, and Kurima Sakai for their contributions to ERICA’s control software, dialog system, and sensor …

Squeezing bottlenecks: exploring the limits of autoencoder semantic representation capabilities
P Gupta, RE Banchs, P Rosso – Neurocomputing, 2016 – Elsevier
… In a neural network based implementation of the autoencoder, the visible layer corresponds to the input x and the hidden layer corresponds to y. There are two variants of autoencoders: (i) with a single hidden layer, and (ii) with multiple hidden layers. …

Character-Level Neural Translation for Multilingual Media Monitoring in the SUMMA Project
G Barzdins, S Renals, D Gosko – arXiv preprint arXiv:1604.01221, 2016 – arxiv.org
… For stream segmentation into the stories it is possible to utilize the exceptional generalization and memorization capacity of the neural networks, which is already applied in the neural dialogue systems such as Gmail Smart Replies (Corrado, 2015; Vinyals&Le, 2015). …

Chinese poetry generation with planning based neural network
Z Wang, W He, H Wu, H Wu, W Li, H Wang… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 2016. How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. arXiv preprint arXiv:1603.08023. … 2010. Recurrent neural network based language model. In INTERSPEECH, volume 2, page 3. …

“Seeing is believing: the quest for multimodal knowledge by Gerard de Melo and Niket Tandon, with Martin Vesely as coordinator”
G de Melo, N Tandon – ACM SIGWEB Newsletter, 2016 – dl.acm.org
… In speech recognition, markedly lower error rates have enabled powerful dialog systems, including Siri on Apple’s iOS, Alexa for Amazon Echo, and … While sufficient for traditional statistical NLP methods of the past, it turns out that deep recurrent neural networks need even more …

Translating player dialogue into meaning representations using LSTMs
J Ryan, AJ Summerville, M Mateas… – … Conference on Intelligent …, 2016 – Springer
… to generate dialogue for our target application, a videogame, and train a long short-term memory (LSTM) recurrent neural network (RNN) to … In service dialogue systems, interaction is constrained and highly structured, lending well to rule-based approaches to natural language …

Domain Adaptation for Neural Networks by Parameter Augmentation
Y Watanabe, K Hashimoto, Y Tsuruoka – arXiv preprint arXiv:1607.00410, 2016 – arxiv.org
… Sun et al. (2015) have proposed an unsupervised domain adaptation method and apply it to the fea- tures from deep neural networks. … Wen et al. (2016) have proposed a procedure to generate natural language for multiple domains of spoken dialogue systems. …

Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot task
F Dernoncourt, JY Lee, TH Bui, HH Bui – arXiv preprint arXiv:1605.02129, 2016 – arxiv.org
… Joint semantic utterance classification and slot filling with recursive neural networks. In Spoken Language Technology Workshop (SLT), 2014 IEEE, pages 554– 559. IEEE, 2014. … In Proceedings of the 7th International Workshop on Spoken Dialogue Systems (IWSDS), 2016. …

MobileSSI: Asynchronous fusion for social signal interpretation in the wild
S Flutura, J Wagner, F Lingenfelser… – Proceedings of the 18th …, 2016 – dl.acm.org
… Encouraged by the recent success of deep neural networks in the area of audio and video processing, Lane et al. … [25]. Long Short-Term Memory Neural Networks have shown great success in paralinguistic tasks (see [3, 35]). …

Unsupervised user intent modeling by feature-enriched matrix factorization
YN Chen, M Sun, AI Rudnicky… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… 17–20. [7] David Griol and Zoraida Callejas, “A neural network ap- proach to … [10] Yun-Nung Chen, William Yang Wang, and Alexander I Rud- nicky, “Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing,” in Proc. …

A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems
D Griol, JA Iglesias, A Ledezma… – International journal of …, 2016 – World Scientific
… spoken dialog system. Keywords: Spoken dialog systems; dialog management; user models; classifier systems; artificial neural networks; clustering; spoken human–machine interaction. 1. Introduction Spoken dialog systems …

Dialogue session segmentation by embedding-enhanced texttiling
Y Song, L Mou, R Yan, L Yi, Z Zhu, X Hu… – arXiv preprint arXiv: …, 2016 – arxiv.org
… pp. 2715–2719. [18] IV Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau, “Building end-to-end dialogue systems using generative hierar- chical neural network models,” arXiv preprint arXiv:1507.04808, 2015. [19] S …

JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio
S Sarkar, D Das, P Pakray, A Gelbukh – Proceedings of SemEval, 2016 – aclweb.org
… measuring the textual sim- ilarity are useful to a broad range of applica- tions including: text mining, information re- trieval, dialogue systems, machine translation … Fea- tures are combined to produces a similarity prediction using both a feedforward and recur- rent neural network. …

Exploiting Sentence and Context Representations in Deep Neural Models for Spoken Language Understanding
LMR Barahona, M Gasic, N Mrkši?, PH Su… – arXiv preprint arXiv: …, 2016 – arxiv.org
… This paper presents a deep learning architecture for the semantic decoder component of a Sta- tistical Spoken Dialogue System. … The proposed architecture uses a convolutional neural network for the sentence representation and a long-short term memory network for the …

“Talking with erica, an autonomous android”
K Inoue, P Milhorat, D Lala, T Zhao… – 17th Annual Meeting of …, 2016 – aclweb.org
… In recent years, dialogue systems have been actively studied in the field of counsel- ing and diagnoses (DeVault et al., 2014). … Afterwards, the output speech signal of the DAE is decoded by an acoustic model based on a deep neural network (DNN). …

Affect Recognition for Web 2.0 Intelligent E-Tutoring Systems: Exploration of Students’ Emotional Feedback
OK Akputu, KP Seng, YL Lee – Psychology and Mental Health: …, 2016 – igi-global.com
… For Litman and Silliman (Litman & Silliman, 2004), in their approach which consists of student dialogue with an ITS framework-Intelligent Tutoring Spoken dialogue system (IT-SPOKE) has been built on a physic text-based tutoring agent called Why2-Atlas (Lithman & Forbes …

Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
C Gulcehre, S Chandar, K Cho, Y Bengio – arXiv preprint arXiv: …, 2016 – arxiv.org
… Despite the success of deep learning in this area (see, eg, [1],) there are still a set of complex tasks that are not well addressed by conventional neural networks. … Memory networks [2] form another family of neural networks with external memory. …

Generating text from structured data with application to the biography domain
R Lebret, D Grangier, M Auli – ArXiv e-prints, March, 2016 – pdfs.semanticscholar.org
… Our model is most similar to Mei et al. (2016) who use an encoder-decoder style neural network model to tackle the Weathergov and Robocup tasks. Their architecture relies on LSTM units and an attention mechanism which reduces scalability compared to our simpler design. …

Improving generalisation to new speakers in spoken dialogue state tracking
I Casanueva, T Hain, P Green – Proceedings of the Annual …, 2016 – eprints.whiterose.ac.uk
… [16] B. Thomson, and S. Young. “Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems”. Computer Speech and Language, 2010. … “Robust dialog state tracking using delexicalised recurrent neural networks and un- supervised adaptation”. …

Natural language generation in dialogue using lexicalized and delexicalized data
S Sharma, J He, K Suleman, H Schulz… – arXiv preprint arXiv: …, 2016 – arxiv.org
… 105–120. Serban, Iulian Vlad et al. (2016). “Building End-To- End Dialogue Systems Using Generative Hierarchical Neural Network Models”. In: AAAI, pp. 3776–3784. Srivastava, Nitish et al. (2014). “Dropout: a simple way to prevent neural networks from overfitting”. …

Does IR Need Deep Learning?,
,H Li – 2016 – hangli-hl.com,
,”… Multimodal Convolutional Neural Networks for Matching Image and Sentence, ICCV 2015. • Pengcheng Yin, Zhengdong Lu, Hang Li, Ben Kao. Neural Enquirer: Learning … A Network-based End-to-End Trainable Task-oriented Dialogue System. arXiv:1604.04562, 2016. …

Information density and overlap in spoken dialogue
N Dethlefs, H Hastie, H Cuayáhuitl, Y Yu… – Computer Speech & …, 2016 – Elsevier
… Our results are relevant for spoken dialogue systems, especially incremental ones. Abstract. … 1. Introduction. Traditionally, the smallest unit of processing in spoken dialogue systems has been a full utterance with strict, rigid turn-taking. …

A Sentence Interaction Network for Modeling Dependence between Sentences
B Liu, M Huang, S Liu, X Zhu… – Proceedings of the 54th …, 2016 – pdfs.semanticscholar.org
… While deep learning methods like Recurrent Neural Network or Convo- lutional Neural Network have been proved to be powerful for sentence modeling, prior studies paid less attention on inter- actions between sentences. …

Gated End-to-End Memory Networks
J Perez, F Liu – arXiv preprint arXiv:1610.04211, 2016 – arxiv.org
… Suppose the original network is a plain feed-forward neural network: y = H(x) (5) where H(x) is a non-linear transformation of its input x. The generic form of Highway Networks is formulated as: … This dataset essentially tests the capacity of end-to-end dialog systems to Page 6. …

Assessing user expertise in spoken dialog system interactions
E Ribeiro, F Batista, I Trancoso, J Lopes… – Advances in Speech …, 2016 – Springer
… with the Let’s Go Bus Information System [15], which provides information about bus schedules, through spoken telephonic interaction with a dialog system. … The phones for each utterance were obtained using the neural networks included in the AUDIMUS [12] ASR system. …

Spectral decomposition method of dialog state tracking via collective matrix factorization
J Perez – arXiv preprint arXiv:1606.05286, 2016 – arxiv.org
… Word-based dialog state tracking with recurrent neural networks. In in Proceedings of SIGdial, 2014c. … Unsu- pervised spoken language understanding for a multi-domain dialog system. IEEE Transactions on Audio, Speech & Language Processing, 21(11):2451–2464, 2013. …

Interactive reinforcement learning for task-oriented dialogue management
P Shah, D Hakkani-Tür, L Heck – NIPS 2016 Deep Learning …, 2016 – research.google.com
… ACM. Schatzmann, J., Thomson, B., Weilhammer, K., Ye, H., and Young, S. (2007). Agenda-based user simulation for bootstrapping a pomdp dialogue system. … (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529(7587):484–489. …

LSTM-based Mixture-of-Experts for Knowledge-Aware Dialogues
P Le, M Dymetman, JM Renders – arXiv preprint arXiv:1605.01652, 2016 – arxiv.org
… 2009. Spoken Dialogue Systems. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers. … 2015. Hierarchical neural network gener- ative models for movie dialogues. arXiv preprint arXiv:1507.04808. …

A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition
Z Huang, SM Siniscalchi, CH Lee – Neurocomputing, 2016 – Elsevier
… Cover image Cover image. A unified approach to transfer learning of deep neural networks with applications to speaker adaptation in automatic speech recognition. … A paradigm to transfer learning of deep neural networks in automatic speech recognition systems is presented. • …

A Wizard-of-Oz Study on A Non-Task-Oriented Dialog Systems That Reacts to User Engagement
Z Yu, L Nicolich-Henkin, AW Black… – 17th Annual Meeting of …, 2016 – aclweb.org
… cO2016 Association for Computational Linguistics A Wizard-of-Oz Study on A Non-Task-Oriented Dialog Systems That Reacts to User … et al., 2011), retrieval-based response selection (Banchs and Li, 2012), and sequence-to-sequence recurrent neural network (Vinyals and Le …

Improving Neural Language Models with a Continuous Cache
E Grave, A Joulin, N Usunier – arXiv preprint arXiv:1612.04426, 2016 – arxiv.org
… Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of gated re- current neural networks on sequence modeling. … Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931, 2015. …

Natural language model re-usability for scaling to different domains
YB Kim, A Rochette, R Sarikaya – … of the Empiricial Methods in Natural …, 2016 – aclweb.org
… Also, we plan to extend the constrained decoding idea to slot tag- ging with neural networks (Kim et al., 2016), which achieved gains over CRFs. References … 2015. Enriching word embed- dings using knowledge graph for semantic tagging in conversational dialog systems. …

A user-centric design of service robots speech interface for the elderly
N Wang, F Broz, A Di Nuovo, T Belpaeme… – Recent Advances in …, 2016 – Springer
… Lemon, O., Georgila, K., Henderson, J., Stuttle, M.: An ISU dialogue system exhibiting reinforcement learning of dialogue policies: generic slot-filling in the TALK in-car … Sainath, T., Mohamed, A., Kingsbury, B., Ramabhadran, B.: Deep convolutional neural networks for LVCSR. …

Deep learning of audio and language features for humor prediction
D Bertero, P Fung – … on Language Resources and Evaluation (LREC), 2016 – lrec-conf.org
… We used the development set to tune the hyperparameters, and in the case of the neural networks to determine the early stopping condition … Our ultimate goal is to integrate laughter response prediction in a machine dialog system, to allow it to understand and react to humor. …

“Your Paper has been Accepted, Rejected, or Whatever: Automatic Generation of Scientific Paper Reviews”
A Bartoli, A De Lorenzo, E Medvet, F Tarlao – International Conference on …, 2016 – Springer
… Gasic, M., Mrkši?, N., Su, PH, Vandyke, D., Young, S.: Semantically conditioned LSTM-based natural language generation for spoken dialogue systems, pp. 1711–1721, September 2015. 17. Sutskever, I., Vinyals, O., Le, QV: Sequence to sequence learning with neural networks. …

Predicting second language proficiency level using linguistic cognitive task and machine learning techniques
YW Yang, WH Yu, HS Lim – Wireless Personal Communications, 2016 – Springer
… In their work, oral data obtained through a spoken dialogue system were rated by human judges and the system, and the results were compared for similarity and accuracy. … 5 Predicting Using Machine Learning Techniques. 5.1 Multi-layer Perceptron Neural Networks. …

Learning to Start for Sequence to Sequence Architecture
Q Zhu, W Zhang, L Zhou, T Liu – arXiv preprint arXiv:1608.05554, 2016 – arxiv.org
… 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceed- ings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16). [Shang et al.2015] Lifeng Shang, Zhengdong Lu, and Hang Li. 2015. …

Conversational engagement recognition using auditory and visual cues
Y Huang, E Gilmartin, N Campbell – Proceedings of Interspeech, 2016 – researchgate.net
… ness dialogue system named TickTock [14], where engagement analysis constituted an important part of the dialogue system al- lowing … with Principal Component Analysis (PCA), de- tailed facial movements, loudness shape features, and convo- lutional neural networks (CNN). …

Multi-view response selection for human-computer conversation
X Zhou, D Dong, H Wu, S Zhao, R Yan, D Yu, X Liu… – EMNLP’16, 2016 – ir.hit.edu.cn
… Previous Deep Neural Network (DNN) based ap- proaches to response selection represent context and response as two embeddings. … Hu et al., (2014) improved the performance using Convolutional Neural Networks (CNN) (LeCun et al., 1989). In 2015, a further …

Speed vs. accuracy: Designing an optimal asr system for spontaneous non-native speech in a real-time application
AV Ivanov, PL Lange, D Suendermann-Oeft… – Proc. of the IWSDS, …, 2016 – oeft.de
… M., Neutatz, F., Schmidt, D.: HALEF: An Open-Source Standard-Compliant Telephony-Based Modular Spoken Dialog System – A Review and … Zhang, X., Trmal, J., Povey, D., Khudanpur, S.: Improving Deep Neural Network Acoustic Models using Generalized Maxout Networks. …

Distributional semantics for understanding spoken meal descriptions
M Korpusik, C Huang, M Price… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… ASRU, Dec. 2011. [24] M. Korpusik, “Spoken language understanding in a nutrition dialogue system,” MS thesis, Massachusetts Institute of Tech- nology, 2015. … ACL, 2014. [30] G. Mesnil, X. He, L. Deng, and Y. Bengio, “Investigation of recurrent-neural-network architectures and …

Syntax or semantics? knowledge-guided joint semantic frame parsing
YN Chen, D Hakanni-Tür, G Tur, A Celikyilmaz, J Guo… – 2016 – csie.ntu.edu.tw
… Spoken language understanding (SLU) is a core component of a spo- ken dialogue system, which involves intent prediction and slot filling and also called semantic frame parsing. Recently recurrent neural networks (RNN) obtained strong results on SLU due to their superior …

RACAI Entry for the IWSLT 2016 Shared Task
S Pipa, AF Vasile, I Ionascu… – Proceedings of the …, 2016 – workshop2016.iwslt.org
… Text normalization is extremely important for automatic machine translation (MT), speech-to-speech translation, information extraction, dialog systems, etc. … text normalization is a hybrid approach using an n-gram model for truecasing and a deep neural network (DNN) …

“A Simple, Fast Diverse Decoding Algorithm for Neural Generation”
J Li, W Monroe, D Jurafsky – arXiv preprint arXiv:1611.08562, 2016 – arxiv.org
Page 1. A Simple, Fast Diverse Decoding Algorithm for Neural Generation Jiwei Li, Will Monroe and Dan Jurafsky Computer Science Department, Stanford University, Stanford, CA, USA jiweil,wmonroe4,jurafsky@stanford.edu Abstract …

Toward conscious-like conversational agents
M Gnjatovi?, B Borovac – … Socially Believable Behaving Systems-Volume II, 2016 – Springer
… also [25, pp.180–2]). To illustrate this, we consider two spoken dialogue systems: the SUNDIAL system designed to handle travel conversations [4], and the speech translation system Verbmobil [1]. The dialogue structure underlying the implementation of the SUNDIAL system is …

Towards Modeling Confidentiality in Persuasive Robot Dialogue
ID Addo, SI Ahamed, WC Chu – … Conference on Smart Homes and Health …, 2016 – Springer
… Act’s (HIPAA) compliance rules [1] will apply when personal health information is collected and stored by the dialogue system. … set of input features: Support Vector Machines (SVM), Boosted Decision Tree, Regression Trees, Random Forests, Neural Networks, and Nearest …

Statistical natural language generation from tabular non-textual data
J Mahapatra, SK Naskar… – The 9th International …, 2016 – aclweb.org
… Brian Langner and Alan W Black. 2009. Mountain: a translation-based approach to natural language gener- ation for dialog systems. Kathleen McKeown, Karen Kukich, and James Shaw. 1994. … 2010. Re- current neural network based language model. …

Controlling the voice of a sentence in japanese-to-english neural machine translation
H Yamagishi, S Kanouchi, T Sato… – Proceedings of the 3rd …, 2016 – aclweb.org
… 5 Related Work An NMT framework consists of two recurrent neural networks (RNNs), called the RNN encoder-decoder, proposed by Cho et al.(2014) and Sutskever et al.(2014). … For example, one may prefer a polite expression for generating conversation in a dialog system. …

Summarizing source code using a neural attention model
S Iyer, I Konstas, A Cheung, L Zettlemoyer – … of the 54th Annual Meeting of the … – aclweb.org
… These approaches are not learning based, and require significant manual template-engineering efforts. We use recurrent neural networks (RNN) based on LSTMs and neural attention to jointly model source code and NL. … (2015) generate text for spoken dialogue systems with a …

The Changing Technological Context of Decision Support Systems
S Kaparthi – Context-Sensitive Decision Support Systems, 2016 – books.google.com
… System Model Model Model Model System System System System Dialogue Dialogue Dialogue Dialogue Dialogue Dialogue System System System … His research interests are in decision support and expert systems, neural networks, and applications of artificial intelligence …

Medical examination data prediction using simple recurrent network and long short-term memory
HG Kim, GJ Jang, HJ Choi, M Kim, YW Kim… – Proceedings of the Sixth …, 2016 – dl.acm.org
… ABSTRACT In this work, we use two different types of recurrent neural networks (RNNs) to predict medical examination results of a subject given the previous measurements. … CCS Concepts •Computing methodologies ? Neural networks; ?Corresponding author …

An overview of end-to-end language understanding and dialog management for personal digital assistants
R Sarikaya, P Crook, A Marin, M Jeong… – IEEE Workshop on …, 2016 – microsoft.com
… Khan, JP Robichaud, P. Crook, R. Sarikaya, Hypotheses Ranking and State Tracking for a Multi-Domain Dialog System using ASR … 15] P. Xu and R. Sarikaya, “Contextual domain classification in spoken language understanding systems using recurrent neural network, in Proc. …

Learning Interactive Behavior for Service Robots–the Challenge of Mixed-Initiative Interaction
P Liu, DF Glas, T Kanda, H Ishiguro – Proceedings of the workshop on …, 2016 – irc.atr.jp
… and gaze behaviors were recognized in an imitative game using a hidden Markov model [8]. Data-driven dialogue systems have been … In particular, many techniques involving deep neural networks have been developed recently for handling language-related tasks, which are …

A Hierarchical LSTM Model for Joint Tasks
Q Zhou, L Wen, X Wang, L Ma, Y Wang – China National Conference on …, 2016 – Springer
… For example, Chinese word segmentation and POS-tagging, POS-tagging and chunking, intent identification and slot filling in goal-driven spoken language dialogue systems, and so on. … Shi et al. [8] proposed a hybrid model of Recurrent Neural Network (RNN) and …

Topic detection and tracking for conversational content by using conceptual dynamic latent Dirichlet allocation
JF Yeh, YS Tan, CH Lee – Neurocomputing, 2016 – Elsevier
… monologues, whereas dialogue has been examined by research on structured meetings [23] and dialogue systems in education [24]. Zhou et al. proposed a natural language processing model for Chinese that combines heterogeneous deep neural networks with conditional …

ITNLP: Pattern-based short text conversation system at NTCIR-12
Y Liu, C Sun, L Lin, X Wang – … of the 12th NTCIR Conference on …, 2016 – research.nii.ac.jp
… Team Name ITNLP Subtasks Short Text Conversation System (Chinese) Keywords artificial intelligence, dialogue systems, natural language pro- cessing … Based on these feature vectors, another set of features were generated to feed Neural networks. …

“China Brain Project: basic neuroscience, brain diseases, and brain-inspired computing”
M Poo, J Du, NY Ip, ZQ Xiong, B Xu, T Tan – Neuron, 2016 – Elsevier
… At the level of computational models, artificial neural network algorithms with more biological plausible learning mechanism will be explored. … meet great challenges for more open and ill-defined tasks like natural language understanding, human dialog system, general visual …

Comparing dialogue strategies for learning grounded language from human tutors
Y Yu, O Lemon, A Eshghi – SEMDIAL 2016 JerSem, 2016 – semantics.rutgers.edu
… hw. ac. uk Arash Eshghi Interaction Lab Heriot-Watt University a. eshghi@ hw. ac. uk Abstract We address the problem of interac- tively learning perceptually grounded word meanings in a multimodal dialogue system. Human …

Recent advances in nonlinear speech processing
A Esposito, M Faundez-Zanuy, AM Esposito… – … innovation, systems and …, 2016 – Springer
… modes that account for gestures, emotions, and social signal processing for developing friendly and socially believable interactive dialogue systems. … Tulics, Dávid Sztahó, Anna Esposito and Klára Vicsi Part IV Improving VUI Constructing a Deep Neural Network Based Spectral …

DeepSoft: A vision for a deep model of software
HK Dam, T Tran, J Grundy, A Ghose – Proceedings of the 2016 24th ACM …, 2016 – dl.acm.org
… state-of-the-art deep learning-based NLP tech- niques [5] such as word2vec, paragraph2vec or Convolutional Neural Networks (CNNs; used in … Given the recent successes in NLP [5] (machine translation, question answering, and dialog systems) and vi- sion [4] (image/video …

“Fbk-hlt-nlp at semeval-2016 task 2: A multitask, deep learning approach for interpretable semantic textual similarity”
S Magnolini, A Feltracco, B Magnini – Proceedings of SemEval, 2016 – aclweb.org
… We use a single neural network classification model for predicting the alignment at chunk level, the re- lation type of the … Headlines), and a question-answer dataset collected and annotated during the evaluation of the BEETLE II tutorial dialogue system (Student Answers …

Laughter and Smile Processing for Human-Computer Interactions
K El Haddad, H Cakmak… – Just talking-casual …, 2016 – pdfs.semanticscholar.org
… Being able to use these expressions with these social func- tionalities in dialogue systems will increase the naturalness of an agent’s reaction during an interaction. Page 2. … al. (Knox and Mirghafori, 2007) presents an auto- matic audio laughter detection using a neural network. …

Real-time language-independent algorithm for dialogue agents
J Arnaud – ?????, 2016 – jstage.jst.go.jp
… In another way, the graph can be compared to a neural network, used for example for modeling sentences?12?; nodes which can be used … corpus of example sentences in the target language, and the system output generation is similar to an example?base dialogue system ?16 …

Compressing neural language models by sparse word representations
Y Chen, L Mou, Y Xu, G Li, Z Jin – arXiv preprint arXiv:1610.03950, 2016 – arxiv.org
… erate new sentences from a neural LM, benefit- ing various downstream tasks like machine trans- lation, summarization, and dialogue systems (De- vlin … First, with a wider application of neural networks in resource- restricted systems (Hinton et al., 2015), such ap- proach is too …

SiAM-dp: an open development platform for massively multimodal dialogue systems in cyber-physical environments,
,R Neßelrath – 2016 – scidok.sulb.uni-saarland.de,
,”Page 1. SiAM-dp: An open development platform for massively multimodal dialogue systems in cyber-physical environments Robert Neßelrath … 21 2.2.5 Presentation Planning and Multimodal Fission . . . . . 24 2.3 Dialogue Systems . . . . . …

Multimodal emotion recognition with evolutionary computation for human-robot interaction
LA Perez-Gaspar, SO Caballero-Morales… – Expert Systems with …, 2016 – Elsevier
… A dialogue system was developed for interaction with a humanoid robot … Recent research has addressed the emotion recognition problem with techniques such as Artificial Neural Networks (ANNs)/Hidden Markov Models (HMMs) and reliability of proposed approaches has been …

Length bias in Encoder Decoder Models and a Case for Global Conditioning
P Sountsov, S Sarawagi – arXiv preprint arXiv:1606.03402, 2016 – arxiv.org
… The most popular neural network for probabilis- tic modeling of sequences in the above applications is the encoder-decoder (ED) network (Sutskever et al., 2014). … Pr(yt|y1,…,yt?1,vx,?) = P(yt|st,?), (1) where st is a state vector implemented using a recur- rent neural network as …

Real-time understanding of complex discriminative scene descriptions
R Manuvinakurike, C Kennington… – Proceedings of the …, 2016 – pub.uni-bielefeld.de
… This is an important practical capability for present and future interactive spoken dialogue systems. There is a trend toward increasing de- ployment of spoken dialogue systems for smart- phones, tablets, automobiles, TVs, and …

JNDSLAM: A SLAM extension for speech synthesis
R Dall, X Gonzalvo – Proc. Speech Prosody, 2016 – cstr.inf.ed.ac.uk
… In an initial investigation into predicting the labels Long Short Term Memory Neural Networks (LSTM) [28] were used … This has potential uses in dialogue systems where we know what we wish to emphasise, whether we are asking a questions etc., phenomena which has specific …

Easy things first: Installments improve referring expression generation for objects in photographs
S Zarrieß, D Schlangen – … of the 54th Annual Meeting of the …, 2016 – pub.uni-bielefeld.de
… Sina Zarrieß David Schlangen Dialogue Systems Group // CITEC // Faculty of Linguistics and Literary Studies Bielefeld University, Germany first.last@uni-bielefeld.de Abstract … Mao et al. (2015) use a convolutional neural network and an LSTM to generate REs directly and on …

DocChat: an information retrieval approach for chatbot engines using unstructured documents
Z Yan, N Duan, J Bao, P Chen, M Zhou, Z Li, J Zhou – 2016 – aclweb.org
… websites. 4.3 Sentence-level Feature We first present an attention-based sentence em- bedding method based on a convolution neural network (CNN), whose input is a sentence pair and output is a sentence embedding pair. Two …

Exploiting turn-taking temporal evolution for personality trait perception in dyadic conversations
MH Su, CH Wu, YT Zheng – IEEE/ACM Transactions on Audio, …, 2016 – ieeexplore.ieee.org
… The first phase includes the recurrent neural networks (RNNs) [23]–[25] and it entails generating the BFI item scores for each turn of a speaker (speaker turn). During a conver- sation, an individual spoken text might be projected onto a 10-dimensional point in the BFI space (Fig. …

A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding
X Zhang, H Wang – 2016 – pdfs.semanticscholar.org
… Recurrent neural networks (RNNs) have been proved effective in SF, while there is no prior work using RNNs in ID. … Spoken language understanding (SLU) in human/machine spoken dialog systems aims to automatically identify the in- tent of the user as expressed in natural …

Introducing a pictographic language for envisioning a rich variety of enactive systems with different degrees of complexity
RK Moore – International Journal of Advanced Robotic …, 2016 – journals.sagepub.com
,,

Detecting paralinguistic events in audio stream using context in features and probabilistic decisions
R Gupta, K Audhkhasi, S Lee, S Narayanan – Computer Speech & …, 2016 – Elsevier
… interest. Potential techniques include Markov models (Rabiner and Juang, 1986), recurrent neural networks (Funahashi and Nakamura, 1993) and linear chain conditional random fields (Lafferty et al., 2001). For instance, Cai et al. …

An empirical investigation of word class-based features for natural language understanding
A Celikyilmaz, R Sarikaya, M Jeong… – IEEE/ACM Transactions …, 2016 – dl.acm.org
… Turian et al [16], [30] uses neural networks in a non-probabilistic lan- guage modeling framework. … The first set is internally collected multimedia data from live deployment scenarios of a spoken dialog system designed for entertainment search for Xbox One game console. …

Multimodal semantic learning from child-directed input
A Lazaridou, G Chrupa?a, R Fernández… – Proceedings of NAACL- …, 2016 – m-mitchell.com
… tract a 4096-dimensional visual vector using the Caffe toolkit (Jia et al., 2014), together with the pre- trained convolutional neural network of Krizhevsky … Our work is also related to research on reference resolution in dialogue systems, such as Kennington and Schlangen (2015). …

Comparing system-response retrieval models for open-domain and casual conversational agent
F Charras, G Dubuisson Duplessis… – … on Chatbots and …, 2016 – workshop.colips.org
… While there are more and more available data for building data-driven dialogue systems (see, eg, the extensive study by Serban et al. … Word and utterance embeddings are jointly learnt as the coefficients of a shallow neural network trained to predict a word, given its context and …

Higher-order Multivariable Polynomial Regression to Estimate Human Affective States
J Wei, T Chen, G Liu, J Yang – Scientific reports, 2016 – nature.com
… From direct observations, facial, vocal, gestural, physiological, and central nervous signals, estimating human affective states through computational models such as multivariate linear-regression analysis, support vector regression, and artificial neural network, have been …

A Keyword-Aware Language Modeling Approach to Spoken Keyword Search
IF Chen, C Ni, BP Lim, NF Chen, CH Lee – Journal of Signal Processing …, 2016 – Springer
Page 1. A Keyword-Aware Language Modeling Approach to Spoken Keyword Search I-Fan Chen1 & Chongjia Ni2 & Boon Pang Lim2 & Nancy F. Chen2 & Chin-Hui Lee1 Received: 13 November 2014 /Revised: 16 February …

Action-coordinating prosody
NG Ward, S Abu – Speech Prosody, 2016 – pdfs.semanticscholar.org
… [35] K. Laskowski, “Auto-imputing radial basis functions for neural- network turn-taking models,” in Interspeech, 2015, pp. 1820– 1824. … [42] NG Ward and D. DeVault, “Ten challenges in highly-interactive dialog systems,” in AAAI Symposium on Turn-taking and Coordi- nation in …

“Research data supporting”” Conditional Generation and Snapshot Learning in Neural Dialogue Systems”””
TH Wen, N Mrksic, S Young – 2016 – repository.cam.ac.uk
… https://doi.org/10.17863/CAM.6142. Description. Cambridge restaurant dialogue domain dataset collected for developing neural network based dialogue systems. The two papers published based on this dataset are: 1. A Network …

McGill Reasoning & Learning Lab: Research Overview,
,R Lowe – cs.mcgill.ca,
,”… “Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.” EMNLP 2014. Serban, Sordoni, Bengio, Courville, Pineau. “Building End-to-End Dialogue Systems using Generative Hierarchical Neural Network Models” AAAI, 2015. …

“Research Data Supporting”” Multi-domain Neural Network Language Generation for Spoken Dialogue Systems”””
W Tsung-Hsien, M Gasic, N Mrksic, R Barahona… – 2016 – repository.cam.ac.uk
This is a natural language generation dataset collected from Amazon Mechanical Turk used in this paper”” Multi-domain Neural Network Language Generation for Spoken Dialogue Systems”” in NAACL-HLT 2016. It contains two domains regarding to consumer electronics:

A multichannel convolutional neural network for cross-language dialog state tracking
H Shi, T Ushio, M Endo, K Yamagami… – … Workshop (SLT), 2016 …, 2016 – ieeexplore.ieee.org
… We consider this a desirable property for building a tracker in the cross-language context, as not every developer will be familiar with both languages. Index Terms- Convolutional neural networks, multi- channel architecture, dialog state tracking, dialog systems …

Generative Deep Neural Networks for Dialogue: A Short Review
IV Serban, R Lowe, L Charlin, J Pineau – arXiv preprint arXiv:1611.06216, 2016 – arxiv.org
… Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI, pages 3776–3784, 2016b. … Learning from real users: Rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems. In SIGDIAL, 2015. …

MULTI-CRITERIA SELF-ADJUSTING GENETIC PROGRAMMING FOR DESIGN NEURAL NETWORK MODELS IN THE TASK OF FEATURE SELECTION
E Loseva – ??????????? ???? ??????, 2016 – elibrary.ru
… shows optimal result after test, what is confirmed in the Table 3. This algorithm allows to find relevant features set by applying compact and accurate neural networks. This approach is useful for those category of tasks, also can be useful to implement in dialog system and to …

Neural Network Approaches to Dialog Response Retrieval and Generation
NIO Lasguido, S Sakti, G Neubig… – … on Information and …, 2016 – search.ieice.org
… SUMMARY In this work, we propose a new statistical model for build- ing robust dialog systems using neural networks to either retrieve or gen- erate dialog response based on an existing data sources. In the retrieval task, we …

Leveraging Recurrent Neural Networks for Multimodal Recognition of Social Norm Violation in Dialog
T Zhao, R Zhao, Z Meng, J Cassell – arXiv preprint arXiv:1610.03112, 2016 – arxiv.org
… [12] Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. Recurrent neural network regularization. … Automatic recognition of conver- sational strategies in the service of a socially-aware dialog system. In 17th Annual SIGDIAL Meeting on Discourse and Dialogue, 2016. …

Towards an end to end Dynamic Dialogue System,
,V Bhalla – researchgate.net,
,”… The multi-fold increase in the use of conversational dialogue systems and its research incorporating all innovations of neural networks and linguistic models in the recent years has led to huge investment from companies that now see potential in multifarious applications and …

Neural Emoji Recommendation in Dialogue Systems
R Xie, Z Liu, R Yan, M Sun – arXiv preprint arXiv:1612.04609, 2016 – arxiv.org
… To the best of our knowledge, our model is the first attempt on emoji classification by taking the contextual information into con- sideration in multi-turn dialogue systems. We attempt to utilize neural networks to learn dia- logue representations, among which the recurrent neural …

Real-Time Speech Emotion and Sentiment Recognition for Interactive Dialogue Systems
D Bertero, FB Siddique, CS Wu, Y Wan, RHY Chan… – aclweb.org
… I would like to travel with you. Meanwhile, dialogue systems like this need to have real-time recognition of user emotion and sen- timent. … We train deep neural network hidden Markov models (DNN-HMMs) using the raw audio together with encode-decode parallel audio. …

TSUNG-HSIEN WEN,
,D EDUCATION Ph – pdfs.semanticscholar.org,
,”… [1] Tsung-Hsien Wen, M. Gasic, N. Mrksic, LM R-Barahona, P.-h. Su, D. Vandyke, S. Young, “Multi-domain Neural Network Language Generation for Spoken Dialogue Systems”, In Proc. NAACL-HLT, San Diego, USA, June 2016. …

Sequence-to-Sequence Learning for End-to-End Dialogue Systems,
,J Van Landeghem – 2016 – researchgate.net,
,”… [9] A Network-based End-to-End Trainable Task-oriented Dialogue System [10] Incorporating Unstructured Textual Knowledge Sources into Neural Dialogue Systems [11] A Neural Network Approach to Context-Sensitive Generation of Conversational Responses …

“Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition”
P Fung, A Dey, FB Siddique, R Lin, Y Yang, D Bertero… – pdfs.semanticscholar.org
… As the availability of interactive dialogue systems is on a rise, people are getting more accustomed to talking to machines. … Therefore, we use a Convolutional Neural Network (CNN) model that bypasses the feature extraction and extracts emotion from raw-audio in real-time. 278 …

A Deep Learning Methodology for Semantic Utterance Classification in Virtual Human Dialogue Systems
D Datta, V Brashers, J Owen, C White… – … Conference on Intelligent …, 2016 – Springer
… describes the development of a deep learning methodology for semantic utterance classification (SUC) for use in domain-specific dialogue systems. … semantic class and the word sequence in an utterance, we have proposed a shallow convolutional neural network (CNN) along …

Spoken keyword detection using recurrent neural network language model
S Koike, A Lee – The Journal of the Acoustical Society of America, 2016 – asa.scitation.org
… Recurrent neural network language model (RNNLM) is introduced as linguistic constraint for both filler-filler and filler-keyword instead of N-gram, and experimental result on actual spoken data for a spoken dialogue system showed that our method can improve the keyword …

LVCSR System on a Hybrid GPU-CPU Embedded Platform for Real-Time Dialog Applications
AV Ivanov, PL Lange… – 17th Annual Meeting of the …, 2016 – aclweb.org
… The upstream dialog system com- ponent receives the recognition result either on a per utterance basis or as a partial feedback up un … Specif- ically, we have used the Deep Neural Network– Weighted Finite State Transducer (DNN-WFST) hybrid with i-vector acoustic adaptation. …

Quote Recommendation in Dialogue using Deep Neural Network
H Lee, Y Ahn, H Lee, S Ha, S Lee – … of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… Glove: Global vectors for word representation. In EMNLP, 2014. [8] IV Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models. In ICASSP, 2013. [9] A. Severyn and A. Moschitti. …

Using a dialogue system based on dialogue maps for computer assisted second language learning
SK Choi, OW Kwon, YK Kim, Y Lee – CALL communities and …, 2016 – books.google.com
… Using a dialogue system based on dialogue maps… on Partially Observable Markov Decision Processes (POMDP) and Deep Neural Networks (DNN) that can make speech recognition and the language understanding robust (Henderson, Thomson, & Young, 2013; Young et al …

International Conference on Oriental Thinking and Fuzzy Logic: Celebration of the 50th Anniversary in the era of Complex Systems and Big Data
BY Cao, PZ Wang, ZL Liu, YB Zhong – 2016 – books.google.com
… 415 Pei-Hua Wang, Hai-Tao Lin and Xiao-Peng Yang Part IV Factor Space and Factorial Neural Networks Factor Space and Normal Context on Concept Description … 687 Ruihua Geng Multi-sentence Level Natural Language Generation for Dialogue System….. …

Reading Comprehension using Entity-based Memory Network
X Wang, K Sudoh, M Nagata, T Shibata… – arXiv preprint arXiv: …, 2016 – arxiv.org
… evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014) 3. Dodge, J., Gane, A., Zhang, X., Bordes, A., Chopra, S., Miller, A., Szlam, A., Weston, J.: Evaluating prerequisite qualities for learning end-to-end dialog systems. …

An Optimization Method Using Clustering Technique for the Human Emotions Detection Artificial Neuro-Fuzzy Logic System
O Murad, M Malkawi – INTERNATIONAL JOURNAL – researchgate.net
… [4] Burkhardt, F., Van Ballegooy, M., Engelbrecht, KP, Polzehl, T., & Stegmann, J. (2009, September). Emotion detection in dialog systems: applications, strategies and challenges. … [6] Demuth, Howard, and Mark Beale. “”Neural network toolbox for use with MATLAB.”” (1993). …

Sequential Match Network: A New Architecture for Multi-turn Response Selection in Retrieval-based Chatbots
Y Wu, W Wu, M Zhou, Z Li – arXiv preprint arXiv:1612.01627, 2016 – arxiv.org
… The vectors are then accumulated in a chronological order through a recur- rent neural network (RNN) which mod- els the relationships among the utterances. … (2016) who utilize a hierarchical recurrent neural network to model utterance relationships. …

Contextual LSTM: A Step towards Hierarchical Language Modeling
S Ghosh, O Vinyals, B Strope, S Roy, T Dean, L Heck – 2016 – research.google.com
… In this paper, we present CLSTM (Contextual LSTM), an extension of the recurrent neural network LSTM (Long-Short Term … a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems. …

TEXT NORMALIZATION FOR AUTOMATIC SPEECH RECOGNITION SYSTEMS
AF VASILE, T BORO? – ISSN 1843-911X – consilr.info.uaic.ro
… In Proceedings of the COLING/ACL on Main conference poster sessions, 33-40. Bang, J., Park, S., Lee, GG (2015). ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications. In Page 135. …

Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network
H Khanpour, N Guntakandla, R Nielsen – aclweb.org
… Many applications benefit from the use of automatic dialogue act classi- fication such as dialogue systems, machine translation, Automatic … in DA classification, from Bayesian Networks (BN) and Hidden Markov Models (HMM) to feed forward Neural Networks, Decision Trees (DT …

Estimating the User’s State before Exchanging Utterances Using Intermediate Acoustic Features for Spoken Dialog Systems
Y Chiba, T Nose, M Ito, A Ito – IAENG International Journal of Computer …, 2016 – iaeng.org
… the WOZ method, as much natural dialog data as possible is collected by having the user converse with a simulated dialog system. … features belonging to the novel inventory were denoted as S2 (manual) and intermediate features estimated by the neural network were denoted …

Using phone features to improve dialogue state tracking generalisation to unseen states
I Casanueva, T Hain, M Nicolao… – Proceeding of SIGDIAL …, 2016 – eprints.whiterose.ac.uk
… 1In a slot based dialogue system the dialogue states are defined as the set of possible value combinations for each slot … this paper, we propose a method to use ASR and phone-related general features to im- prove the generalisation of a Recurrent Neural Network (RNN) based …

“Dialog state tracking, a machine reading approach using Memory Network”
J Perez, F Liu – arXiv preprint arXiv:1606.04052, 2016 – arxiv.org
… In an end-to-end dialog system, the aim of dialog state tracking is to accurately estimate a compact representation of the current … of machine reading and proposes to solve it using an End-to-End Memory Network, MemN2N, a memory-enhanced neural network architecture. …

Optimising spoken dialogue systems using Gaussian process reinforcement learning for a large action set
TFW Nicholson, M Gaši? – mlsalt.eng.cam.ac.uk
… This is important in task-orientated dialog systems in which it is assumed that the user has a desire to perform a … on moving away from explicit distributions over ontology-extracted slots towards implicit distributed repre- sentations as produced from a neural network [20], and to …

Topic Aware Neural Response Generation
C Xing, W Wu, Y Wu, J Liu, Y Huang, M Zhou… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Although previous re- search focused on dialog systems, recently, with the large amount of conversation data available on the Internet, chat- bots are becoming hot in both … Recently, neural network based methods have become the mainstream because of their capability to …

DialPort: Connecting the Spoken Dialog Research Community to Real User Data
T Zhao, K Lee, M Eskenazi – arXiv preprint arXiv:1606.02562, 2016 – arxiv.org
… Goal-driven dialog systems focus on a set of predefined in-domain requests, non- goal driven dialog systems must handle open do … Recently, recurrent neural network-based data- driven approaches have been proposed that train on large movie transcription corpora (Table 2 …

Neural Dialog State Tracker for Large Ontologies by Attention Mechanism
Y Jang, J Ham, BJ Lee, Y Chang… – IEEE Workshop on …, 2016 – ailab.kaist.ac.kr
… 2579–2605, 2008. [9] Hongjie Shi, Takashi Ushio, Mitsuru Endo, Katsuyoshi Yamagami, and Noriaki Horii, “Convolutional neural networks for multi-topic dialog state tracking,” in Pro- ceedings of the 7th International Workshop on Spoken Dialogue Systems (IWSDS), 2016. …

End-to-End Joint Learning of Natural Language Understanding and Dialogue Manager
X Yang, YN Chen, D Hakkani-Tur, P Crook, X Li… – arXiv preprint arXiv: …, 2016 – arxiv.org
… recurrent neural networks,” in SIGDIAL, 2014, pp. 292–299. [23] M Gašic, Catherine Breslin, Matthew Henderson, Dongho Kim, Martin Szummer, Blaise Thomson, Pirros Tsiakoulis, and Steve Young, “On-line policy optimi- sation of Bayesian spoken dialogue systems via human …

Sequence Generation & Dialogue Evaluation,
,R Lowe – cs.mcgill.ca,
,”… Driven Dialogue Systems.” 2016. Serban, Sordoni, Lowe, Pineau, Courville, Bengio. “A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues.” AAAI, 2017. Sutskever, Vinyals, Le. “Sequence-to-sequence Learning with Neural Networks.” NIPS, 2014. …

Context-aware Natural Language Generation with Recurrent Neural Networks
J Tang, Y Yang, S Carton, M Zhang, Q Mei – arXiv preprint arXiv: …, 2016 – arxiv.org
… 542. ACM. Mikolov, T., and Zweig, G. 2012. Context dependent recur- rent neural network language model. In SLT, 234–239. Oh, AH, and Rudnicky, AI 2000. Stochastic lan- guage generation for spoken dialogue systems. In …

“An Innovative Optimization Technique for Drowsiness Detection Based on Feature Extraction Capitalizing Neural Network and Sparse Classifiers”” Reena …”
AP Aruthengavilai – Asian Journal of Information Technology, 2016 – docsdrive.com
Asian Journal of Information Technology 15 (14): 2399-2410, 2016 ISSN: 1 682.39 | 5 C Medwell Journals, 201 6 An Innovative Optimization Technique for Drowsiness Detection Based on Feature Extraction Capitalizing Neural Network and Sparse Classifiers “”Reena Daphne …

Dialogue act recognition for Chinese out-of-domain utterances using hybrid CNN-RF
J Wang, P Huang, Q Huang, Z Ke… – … Processing (IALP), 2016 …, 2016 – ieeexplore.ieee.org
… OOD) utterances, dialogue act (DA) recognition for OOD utterances in restricted domain spoken dialogue system remains a great challenge. This paper tackles this problem by proposing an effective DA recognition method using hybrid convolutional neural network (CNN) and …

Introduction to the Special Issue on Dialog State Tracking
JD Williams, A Raux, M Henderson – Dialogue & Discourse, 2016 – xrce.xerox.com
… adopts an established generative- style state update, but estimates two of the core components using a “long short-term memory” (LSTM) neural network. … This is an exciting period for spoken dialogue systems, both in terms of theoretical advances and commercial deployments. …

Sample-efficient Deep Reinforcement Learning for Dialog Control
K Asadi, JD Williams – arXiv preprint arXiv:1612.06000, 2016 – arxiv.org
… Page 2. episodic problem, the return at a timestep t is: Gt = T?t ? i=1 ?i?1rt+i , (1) where T is the terminal timestep, and ? is a discount factor 0 ? ? ? 1. In this paper, we consider policies represented as a recurrent neural network (RNN). … 4 Problem 1: dialog system …

LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices
M Coto-Jiménez, J Goddard-Close – Mexican Conference on Pattern …, 2016 – Springer
… In this paper, we present the application of long short-term memory deep neural networks as a postfiltering step in HMM-based … grown from early systems which aid the visually impaired, to in-car navigation systems, e-book readers, spoken dialog systems, communicative robots …

Challenges in Building Highly-Interactive Dialog Systems
NG Ward, D DeVault – pdfs.semanticscholar.org
… Policy committee for adaptation in multi- domain spoken dialogue systems. In IEEE ASRU, 806–812. Geiger, JT; Eyben, F.; Schuller, B.; and Rigoll, G. 2013. Detecting overlapping speech with long short-term memory recurrent neural networks. In Interspeech, 1668–1672. …

A step beyond local observations with a dialog aware bidirectional GRU network for Spoken Language Understanding
V Vukotic, C Raymond, G Gravier – Interspeech, 2016 – hal.inria.fr
… In the first part, we evaluate different recurrent neural network architectures, such as simple recurrent neural networks (RNN), long short-term … 2.2. MEDIA The research project MEDIA [13] evaluates different SLU mod- els of spoken dialogue systems dedicated to provide tourist in …

SPEECH EMOTION RECOGNITION WITH SKEW-ROBUST NEURAL NETWORKS
PY Shih, CP Chen, HM Wang – iis.sinica.edu.tw
… Index Terms— speech emotion recognition, data imbal- ance, neural networks … For examples of applications, SER can be incorporated in automatic speech recognition sys- tems or spoken dialogue systems to improve recognition ac- curacy or user experience. …

Hashtag Recommendation Using End-To-End Memory Networks with Hierarchical Attention
H Huang, Q Zhang, Y Gong, X Huang – pdfs.semanticscholar.org
… 2015. Evaluating prerequisite qualities for learning end-to-end dialog systems. CoRR, abs/1511.06931. … ACM. Yeyun Gong and Qi Zhang. 2016. Hashtag recommendation using attention-based convolutional neural network. …

Dialog State Tracking and action selection using deep learning mechanism for interview coaching
MH Su, KY Huang, TH Yang, KJ Lai… – Asian Language …, 2016 – ieeexplore.ieee.org
… 330-335. [17] IV Serban, A. Sordoni, Y. Bengio, A. Courville and J. Pineau, “Building end-to-end dialog systems using generative hierarchical neural network models,” in the 30th AAAI Conference on Artificial Intelligence (AAAI-16), Proceedings, 2016, pp. …

LSTM ENCODER–DECODER FOR DIALOGUE RESPONSE GENERATION
Z Yu, C Yuan, X Wang, G Yang – workshop.colips.org
… better performances on multi- domain NLG task [7]. Our approach is mainly based on the recent work on neural network based NLG. … Furthermore, we focus on models which can exploit dialog histories to generate fluent, more human-like utterances for spoken dialogue systems. …

Neural Networks for Natural Language Processing,
,L Mou – sei.pku.edu.cn,
,”… “”Sequence to sequence learning with neural networks.”” NIPS. 2014. … Page 47. SequenceLevel Training ? Motivation: We don’t have the ground truth In a dialogue system, “The nature of of opendomain conversations shows that a variety of replies are plausible, but …

Knowledge Enhanced Hybrid Neural Network for Text Matching
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1611.04684, 2016 – arxiv.org
Page 1. Knowledge Enhanced Hybrid Neural Network for Text Matching Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ †State Key … text. To this end, we propose a knowledge enhanced hy- brid neural network (KEHNN). The …

Joint Online Spoken Language Understanding and Language Modeling with Recurrent Neural Networks
B Liu, I Lane – arXiv preprint arXiv:1609.01462, 2016 – arxiv.org
… Speaker intent detection and semantic slot filling are two critical tasks in spoken lan- guage understanding (SLU) for dialogue systems. In this paper, we describe a re- current neural network (RNN) model that jointly performs intent detection, slot fill- ing, and language modeling. …

Emotion extraction based on multi bio-signal using back-propagation neural network
G Yoo, S Seo, S Hong, H Kim – Multimedia Tools and Applications, 2016 – Springer
… Pattern Recogn 44(3):572–587 12. Haag A, Goronzy S, Schaich P, Williams J (2004) Emotion recognition using bio-sensors: first step towards an automatic system, in affective dialogue systems tutorial and research workshop. … Yegnanarayana B (2004) Artificial neural networks. …

Multimodal deep neural nets for detecting humor in TV sitcoms
D Bertero, P Fung – Spoken Language Technology Workshop ( …, 2016 – ieeexplore.ieee.org
… We showed that our neural network is particularly effective in increasing the F-score of 5 % over a Conditional Random Field baseline on … Adapting and testing our model to other domains will ultimately allow its integration into a machine dialog system capable of recog- nizing …

Neural Discourse Modeling of Conversations
JM Pierre, M Butler, J Portnoff, L Aguilar – arXiv preprint arXiv:1607.04576, 2016 – arxiv.org
… [10] N. Kalchbrenner and P. Blunsom. Recurrent convolu- tional neural networks for discourse compositionality. … The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909, 2015. …

Stylistic Transfer in Natural Language Generation Systems Using Recurrent Neural Networks
J Kabbara, JCK Cheung – EMNLP 2016, 2016 – aclweb.org
… 2014. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112. … 2015. Seman- tically conditioned lstm-based natural language gener- ation for spoken dialogue systems. …

Context-aware Natural Language Generation for Spoken Dialogue Systems
H Zhou, M Huang, X Zhu – aclweb.org
… 2013. Speech recognition with deep recurrent neural networks. In 2013 IEEE international conference on acoustics, speech and signal processing, pages 6645–6649. … Alice H Oh and Alexander I Rudnicky. 2000. Stochastic language generation for spoken dialogue systems. …

Hybrid Dialogue State Tracking for Real World Human-to-Human Dialogues
K Sun, S Zhu, L Chen, S Yao, X Wu, K Yu – Interspeech 2016, 2016 – kaisun.org
… 8. References [1] S. Young, M. Gasic, B. Thomson, and JD Williams, “POMDP- based statistical spoken dialog systems: A review,” Proceedings of the IEEE, vol. 101, no. 5, pp. 1160–1179, 2013. … [8] M. Henderson, B. Thomson, and S. Young, “Deep neural network approach for …

CNTK: Microsoft’s Open-Source Deep-Learning Toolkit
F Seide, A Agarwal – Proceedings of the 22nd ACM SIGKDD …, 2016 – dl.acm.org
… the effectiveness of deep neural networks for recognition of conversational speech. Throughout his career, he has been interested in and worked on a broad range of topics and components of automatic speech recognition, including spoken-dialogue systems, recognition of …

Measuring Heterogeneous User Behaviors During the Interaction with Dialog Systems
D Griol, JM Molina – International Conference on Practical Applications of …, 2016 – Springer
… This decision is modeled by a classification process in which a neural network is used. An additional statistical model has been introduced for errors introduction and confidence measures generation. This way, the dialog system can also be evaluated by considering different …

Expression of emotion using a system combined artificial neural network and memristor-based crossbar array
G Xie, G Liu, S Zhang – Control Conference (CCC), 2016 35th …, 2016 – ieeexplore.ieee.org
… First steps towards an automatic system,” Affective dialogue systems, Springer Berlin Heidelberg, pp. 36-48, 2004. … Conf. on Multimedia and Expo, 2005. [14] B. Cheng and G. Liu, “Emotion recognition from surface EMG signal using wavelet transform and neural network,” J. …

Deep Learning for Natural Language Processing-Research at Noah’s Ark Lab,
,H Li – 2016 – hangli-hl.com,
,”… Deep Match Tree • Based on dependency parsing • Deep neural network for matching, with first layer representing mined matching patterns 13 Wang et al., IJCAI 2015 … Alan Turing Page 27. Natural Language Dialogue System – Retrieval based Approach index of messages …

Text classification for spoken dialogue systems,
,R Sergienko – 2016 – oparu.uni-ulm.de,
,”… consuming, especially for real-time spoken dialogue systems. Therefore, the next step of text classification is dimensionality reduction. … The k nearest neighbors algorithm (k-NN), support vector machines (SVM), and artificial neural networks (ANN) are examples of effective …

Recurrent convolutional neural networks for structured speech act tagging
T Ushio, H Shi, M Endo, K Yamagami… – … Workshop (SLT), 2016 …, 2016 – ieeexplore.ieee.org
Page 1. RECURRENT CONVOLUTIONAL NEURAL NETWORKS FOR STRUCTURED SPEECH ACT TAGGING … We propose local coactivate multi-task learning model for capturing structured speech acts, based on sentence features by recurrent convolutional neural networks. …

Deep neural network based feature extraction using convex-nonnegative matrix factorization for low-resource speech recognition
C Qin, L Zhang – Information Technology, Networking, …, 2016 – ieeexplore.ieee.org
… better with senones (tied triphone states) training targets, which builds a context- dependent deep neural network hidden Markov … phone service, the Repeat After Me speech data collecting process, and telephone interactions with the PublicTransportInfo Spoken Dialog System. …

Using natural language processing (NLP) for designing socially intelligent robots
IA Hameed – … and Learning and Epigenetic Robotics (ICDL- …, 2016 – ieeexplore.ieee.org
… In this abstract, an adaptive and interactive dialogue system is designed to exchange a chat with a user using personal information stored in his/her user profile. … An artificial neural network (ANN) based FD system is used to increase the system’s predictability. …

Context-Sensitive and Role-Dependent Spoken Language Understanding using Bidirectional and Attention LSTMs
C Hori, T Hori, S Watanabe, JR Hershey – Interspeech 2016, 2016 – merl.com
… Spoken language under- standing (SLU) technologies in dialog systems have been inten- sively investigated to estimate the intention of user … Recurrent neural networks (RNNs) have been more actively applied to utterance classification to consider history of a word sequence in …

Learning Through Dialogue Interactions
J Li, AH Miller, S Chopra, MA Ranzato… – arXiv preprint arXiv: …, 2016 – arxiv.org
… In the context of dialogue, with the recent popularity of deep learning models, many neural dialogue systems have been proposed. … As far as we know, current dialogue systems mostly focus on learning through fixed supervised signals rather than interacting with users. …

Enhancements in Assamese spoken query system: Enabling background noise suppression and flexible queries
A Dey, S Shahnawazuddin, KT Deepak… – … (NCC), 2016 Twenty …, 2016 – ieeexplore.ieee.org
… 4, May 1995, pp. 2687–2690. [4] JR Glass, “Challanges for spoken dialogue systems,” in Proc. IEEE ASRU workshop, 1999. … 18, no. 7, pp. 1527–1554, Jul. 2006. [7] G. Dahl, D. Yu, L. Deng, and A. Acero, “Context-dependent pre-trained deep neural networks for large …

Nonparametric Bayesian Models for Spoken Language Understanding
K Wakabayashi, J Takeuchi, K Funakoshi, M Nakano – aclweb.org
… Various sequential labeling algorithms have been applied to this task, including support vector machines, conditional random fields (CRF) (Lafferty et al., 2001; Hahn et al., 2011), and deep neural networks (Mesnil et al., 2015; Xu and Sarikaya, 2013). Vukotic et al. …

Proceedings of the Second International Afro-European Conference for Industrial Advancement AECIA 2015
A Abraham, K Wegrzyn-Wolska, AE Hassanien… – 2016 – books.google.com
… Windows….. 185 Hadhami Kaabi Solar Power Production Forecasting Based on Recurrent Neural Network….. 195 … Corral Page 14. Contents xv Dialogue Systems: Modeling and Prediction of their Dynamics….. 421 …

Prosodic Head Motion Generation from Text for a Chinese Talking Avatar
Y Sun, Y Wang – … Intelligence and Security (CIS), 2016 12th …, 2016 – ieeexplore.ieee.org
… We take Decision Tree algorithm as the baseline and use Neural Network algorithm to check the predicted effect. The Chinese corpus is sourced from Chinese ³CCTV News´. … A dialogue system usually uses a textYdriven approach since the spoNen text is Nnown. …

UT Dialogue System at NTCIR-12 STC
S Sato, S Ishiwatari, N Yoshinaga, M Toyoda… – research.nii.ac.jp
… ABSTRACT This paper reports a dialogue system developed at the Uni- versity of Tokyo for participation in NTCIR-12 on the short … ranking of the chosen candidates in accordance with the perplexity given by Long Short-Term Memory-based Recurrent Neural Network (lstm-rnn …

KALDI Recipes for the Czech Speech Recognition Under Various Conditions
P Mizera, J Fiala, A Brich, P Pollak – International Conference on Text, …, 2016 – Springer
… Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T., Kingsbury, B.: Deep neural networks for acoustic … Sagae, K., Artstein, R., Can, D., Georgiou, P., Narayanan, S., Leuski, A., Traum, D.: Which ASR should I choose for my dialogue system? …

Dialogue Learning With Human-In-The-Loop
J Li, AH Miller, S Chopra, MA Ranzato… – arXiv preprint arXiv: …, 2016 – arxiv.org
… lower test performance is expected. 4Note, this is not the same as a randomly initialized neural network policy, because due to the synthetic construction with an omniscient labeler the labels will be balanced. In our work, we …

“METHOD TO IDENTIFY DEEP CASES BASED ON RELATIONSHIPS BETWEEN NOUNS, VERBS, AND PARTICLES”
D Ide, M Kimura – INTERNATIONAL CONFERENCES ON, 2016 – ERIC
… In the future, we will increase the volume of training data to improve the precision of the neural network and plan to extend a method to estimate omitted words, such as pronouns or zero pronouns, based on the deep … A proposal of topic map based dialog system (2nd report). …

“Crossmodal language grounding, learning, and teaching”
S Heinrich, C Weber, S Wermter, R Xie, Y Lin, Z Liu – 2016 – pdfs.semanticscholar.org
… Our research endeavour includes to develop a) a cortical neural-network model that learns to ground language into crossmodal embodied … challenge: speech recognition is still limited to good signal-to-noise conditions or well adapted models; dialogue systems depend on a …

A User Simulator for Task-Completion Dialogues
X Li, ZC Lipton, B Dhingra, L Li, J Gao… – arXiv preprint arXiv: …, 2016 – arxiv.org
… to sequence learning with neural networks. In NIPS, 2014. [22] Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes, David Vandyke, and Steve Young. Conditional generation and snapshot learning in neural dialogue systems. …

Multi-modal Variational Encoder-Decoders
IV Serban, II Ororbia, G Alexander, J Pineau… – arXiv preprint arXiv: …, 2016 – arxiv.org
… multi-modal distributions — such as the distribution over topics in a text corpus, or natural language responses in a dialogue system — the uni … The idea of using an artificial neural network to approximate an inference model dates back to the 90s (Hinton & Zemel, 1994; Hinton et …

Tamil Speech Word Recognition System with Aid of ANFIS and Dynamic Time Warping (DTW)
S Rojathai, M Venkatesulu – Journal of Computational and …, 2016 – ingentaconnect.com
… Two concepts such as speech recognition histories and acoustic classification have been employed and estimated in a genuine dialogue system used by non … The two models chosen for the task are Hidden Markov Models (HMM) and auto associative neural networks (AANN). …

“Speech and Computer: 18th International Conference, SPECOM 2016, Budapest, Hungary, August 23-27, 2016, Proceedings”
A Ronzhin, R Potapova, G Németh – 2016 – books.google.com
… 140 András Beke and György Szaszák Backchanneling via Twitter Data for Conversational Dialogue Systems….. … 182 Lucie Skorkovská Convolutional Neural Network in the Task of Speaker Change Detection…. 191 Marek Hrúz and Marie Kunešová Page 13. …

Reference Resolution in Situated Dialogue with Learned Semantics
X Li, KE Boyer – 17th Annual Meeting of the Special Interest Group on …, 2016 – aclweb.org
… To achieve this goal, dialogue systems must perform reference resolution, which involves identifying the referents in the environment that the user … We tested logistic regression, decision tree, naive Bayes, and neural networks as classifiers to compute the p (Rk, oi) for each …

Computational Natural Language Learning:±20years±Data±Features±Multimodal±Bioplausible
DMW Powers – CoNLL 2016, 2016 – aclweb.org
… Development of a virtual agent based social tutor for children with autism spectrum disorders. In The 2010 International Joint Conference on Neural Networks (IJCNN), pages 1–9. IEEE. … In Proceedings of the 2010 Workshop on Companionable Dialogue Systems, pages 7–12. …

Speech-based emotion recognition and speaker identification: static vs. dynamic mode of speech representation
M Sidorov, W Minker, ES Semenkin – … . ????? «?????????? ? ??????», 2016 – mathnet.ru
… SI and ER can be used to improve a Spoken Dialogue System (SDS). Furthermore, specific information about a speaker can lead to higher ER accuracy. … Artificial Neural Networks. This class of algorithms is based on the structural and functional modelling of the human brain. …

Statistical Natural Language Generation
TH Wen, M Gasic – 2016 – mi.eng.cam.ac.uk
… On the difficulty of training recurrent neural networks. ICML 2013. • Tsung-Hsien Wen, Milica Gasic , Nikola Mrksic, Pei-Hao Su, David Vandyke, and Steve Young. Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems. In …

Speech Intent Recognition for Robots
B Shen, D Inkpen – … and Computers in Sciences and in Industry …, 2016 – ieeexplore.ieee.org
… 2038-2042, October, 2008. [4] R. Jiang, TY Kee, H. Li, CY Wong, DK Limbu, “Development of Event-Driven Dialogue System for Social Mobile … [8] F. Raue, W. Byeon, TM Breuel, M. Liwicki, “Parallel sequence classification using recurrent neural networks and alignment”, 13th …

Comparing speaker independent and speaker adapted classification for word prominence detection
A Schnall, M Heckmann – Spoken Language Technology …, 2016 – ieeexplore.ieee.org
… Since individual speakers use different ways of expressing prominence, it is not easily extracted and incorporated in a dialog system. … Index Terms— word prominence detection, speaker adaptation, SVM, deep neural network 1. INTRODUCTION …

Dynamic Intelligent Systems Integration and Evolution of Intelligent Control Systems Architectures
VM Rybin, GV Rybina, SS Parondzhanov – Biologically Inspired Cognitive …, 2016 – Springer
… Hybridization methods of dynamic IES with neural networks (neurocontrol), evolutionary methods and genetic modeling and other approaches used for the implementation of intelligent control brings a rather good result. … 2. Intelligent Dialogue Systems. …

Addressee and Response Selection for Multi-Party Conversation
H Ouchi, Y Tsuboi – researchgate.net
… based techniques without such heuristics, which leads to recent work utilizing the SMT-based techniques with neural networks (Shang et al … Basically, the addressee detection has been tackled in the spoken/multimodal dialog system research, and the models largely rely on …

A framework for improving error detection and correction in spoken dialog systems
D Griol, JM Molina – Soft Computing, 2016 – Springer
… For a practical dialog system, the number of possible histories (ie, possible sequences of pairs preceding the current one) is very large. … to solve the previous maximization by means of a classification process, for which the use of artificial neural networks (multilayer perceptrons …

A Sparse Interactive Model for Matrix Completion with Side Information
J Lu, G Liang, J Sun, J Bi – Advances In Neural Information …, 2016 – papers.nips.cc
Paper accepted and presented at the Neural Information Processing Systems Conference (http://nips.cc/).

Deep Reinforcement Learning for Multi-Domain Dialogue Systems
H Cuayáhuitl, S Yu, A Williamson, J Carse – arXiv preprint arXiv: …, 2016 – arxiv.org
… include the following. [6] uses a Recurrent Neural Network (RNN) for dialogue act prediction in a POMDP-based dialogue system, which focuses on mapping system and user sentences to dialogue acts. [2] applies Deep Reinforcement …

Learning User Intentions in Natural Language Call Routing Systems
K Aida-zade, S Rustamov – Recent Developments and New Direction in …, 2016 – Springer
… Neural networks, support vector machines, and radial basis function approaches are also effective in topic identification problems [16]. … They proved that correct identification of user intention seriously improves the dialogue system performance and its decision-making function. …

Varying Ability to Observe in a Partially Observable World,
,A Padmakumar – cs.utexas.edu,
,”… For example, in a dialog system, reinforcement learning is used to identify how to respond to the user in order to satisfy their request as quickly as possible. … A neural network with a single hidden layer is used to model the mapping between ft, ft?1 and c as follows – …

Driver confusion status detection using recurrent neural networks
C Hori, S Watanabe, T Hori… – Multimedia and Expo …, 2016 – ieeexplore.ieee.org
… The LR, DNNs, RNNs and LSTMs were trained using the Chainer neural network toolkit [12]. 4.3. Evaluation Metrics … v. 3, 2000, p. 362-365, 2000. [2] Teruhisa Misu, Antoine Raux, Ian Lane, Joan Devassy, and Rakesh Gupta, “Situated multi-modal dialog system in vehicles,” in …

Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear
ZC Lipton, J Gao, L Li, J Chen, L Deng – sites.google.com
… agents. Some investigated applications include robotics [13], dialogue systems [7, 15], energy management [21], and self- driving cars [23]. … 20]. When training a DQN, we successively update a neural network based on experiences. …

Semantic Expansion of Auto-Generated Scene Descriptions to Solve Robotic Tasks
MA Gutiérrez, RE Banchs – International Journal of …, 2016 – search.proquest.com
… 2015). His recent areas of research include cognitive vision, deep neural networks, multimodal systems and word semantics. … His recent areas of research include Machine language Information Retrieval and Dialogue Systems. More …

The MSIIP system for dialog state tracking challenge 5
Y Su, M Li, J Wu – Spoken Language Technology Workshop ( …, 2016 – ieeexplore.ieee.org
… the application of deep learning has been used as well, like Deep Neural Network (DNN) [3], and Recurrent Neural Network (RNN) [4 … 2] Blaise Thomson and Steve Young, “Bayesian update of dialogue state: A pomdp framework for spoken dialogue systems,” Computer Speech …

Introduction to statistical spoken dialogue systems,
,M Gašic – 2016 – cl.cam.ac.uk,
,”… 17 / 32 Page 18. Machine learning in spoken dialogue systems Data ? Dialogues ? Labelled user intents ? Transcribed speech Model ? Regression ? Classification ? Markov decision process ? Neural networks Predictions ? What the user wants …

Online Sequence-to-Sequence Reinforcement Learning for Open-Domain Conversational Agents
N Asghar, P Poupart, J Xin, H Li – arXiv preprint arXiv:1612.03929, 2016 – arxiv.org
… Nature, 518(7540):529–533, 2015. [Serban et al., 2016a] Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. Building end-to-end dialogue systems using generative hi- erarchical neural network models. In AAAI, pages 3776– 3784, 2016. …

Compositional Sentence Representation from Character within Large Context Text
G Kim, H Lee, J Choi, S Lee – arXiv preprint arXiv:1605.00482, 2016 – arxiv.org
… Many compositional word models based on neural networks have been proposed, and have been used for sentence classi- … Prediction of the DA can be further used as an input to modules in dialogue systems such as dialogue manager. …

“Dialogues with Social Robots: Enablements, Analyses, and Evaluation”
K Jokinen, G Wilcock – 2016 – books.google.com
… Stefan Ultes, Alexander Schmitt and Wolfgang Minker Recurrent Neural Network Interaction Quality Estimation….. 381 Louisa Pragst, Stefan Ultes and Wolfgang Minker An Evaluation Method for System Response in Chat-Oriented Dialogue System….. …

“Conversational In-Vehicle Dialog Systems: The past, present, and future”
F Weng, P Angkititrakul, EE Shriberg… – IEEE Signal …, 2016 – ieeexplore.ieee.org
… putational power available through the cloud, more applica- tions and AI technologies started to be integrated into dialog systems; see Figure … based IPAs has been partly attributed to recent advances in deep learning tech- nologies—especially deep neural networks (DNNs) [16 …

Spoken dialog systems based on online generated stochastic finite-state transducers
LF Hurtado, J Planells, E Segarra, E Sanchis – Speech Communication, 2016 – Elsevier
… page. Speech Communication. Volume 83, October 2016, Pages 81–93. Cover image Cover image. Spoken dialog systems based on online generated stochastic finite-state transducers. … 2. The EDECAN-SPORTS dialog system. A …

Visual Dialog
A Das, S Kottur, K Gupta, A Singh, D Yadav… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Hierarchical Recurrent Encoder: that contains a dialog- level Recurrent Neural Network (RNN) sitting on top of a question-answer (QA)-level recurrent block. In each … [13] is a statistical templated-question gener- ator and not an actual visual dialog system. …

Structured Knowledge and Kernel-based Learning: the case of Grounded Spoken Language Learning in Interactive Robotics
R Basili, D Croce – ceur-ws.org
… or Machine Translation is quite radical in this respect, since the work presented in [2]. However, Neural Networks (NNs) underlying such … Spoken Language Understanding (SLU) in interactive dialogue systems ac- quires a specific nature, when applied in Interactive Robotics. …

Visual Fashion-Product Search at SK Planet
T Kim, S Kim, S Na, H Kim, M Kim, BK Jeon – arXiv preprint arXiv: …, 2016 – arxiv.org
… Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12-17, 2016, Phoenix, Arizona, USA., pp. 3776–3784, 2016. …

NLU vs. Dialog Management: To Whom am I Speaking?
D Schnelle-Walka, S Radomski, B Milde… – Workshop on Smart … – informatik.tu-darmstadt.de
… 18. Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tur, D., He, X., Heck, L., Tur, G., Yu, D., et al. Using recurrent neural networks for slot filling in spoken language understanding. … Research and commercial spoken dialog systems. …

Natural Language Generation through Character-Based RNNs with Finite-State Prior Knowledge
R Goyal, M Dymetman, E Gaussier, U LIG – pdfs.semanticscholar.org
… In particular, architectures based on Recurrent Neural Network (RNN) such as LSTMs (Hochreiter and Schmidhuber, 1997) and GRUs (Cho et al., 2014) have been succesfully used in Language Modelling tasks due to their … (2015) in the context of a dialog system, where the …

Personified Autoresponder,
,A Mahendra – cs224d.stanford.edu,
,”… “A Persona-Based Neural Conversation Model”. In: CoRR abs/1603.06155 (2016). [Ser+16] Iulian Vlad Serban et al. “Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models”. In: AAAI. 2016. [San] Jeffrey Dean Sanjay Ghemawat. …

Backchanneling via Twitter Data for Conversational Dialogue Systems
M Inaba, K Takahashi – International Conference on Speech and …, 2016 – Springer
… Experimental results demonstrated that our method can appropriately select backchannels to given inputs and significantly outperform baseline methods. Keywords. Conversational dialogue systems Recurrent neural network Backchanneling. 1 Introduction. …

On Dialogue Breakdown: Annotation and Detection
K Funakoshi, R Higashinaka, M Inaba, Y Kobayashi… – workshop.colips.org
… including errors and limited capabilities of machines, and are one of the major issues to be addressed in dialogue systems research. … use of NCM, LSTM, bag- of-words embedding, an extended NCM baseline CRF Word frequencies (RNN:Recurrent Neural Network, LSTM:Long …

DialPort: A General Framework for Aggregating Dialog Systems
T Zhao, M Eskenazi, K Lee – EMNLP 2016, 2016 – aclweb.org
… Joelle Pineau. 2015. Build- ing end-to-end dialogue systems using generative hi- erarchical neural network models. arXiv preprint arXiv: 1507.04808. Richard S Sutton, Doina Precup, and Satinder Singh. 1999. Between mdps …

A Dataset of Operator-client Dialogues Aligned with Database Queries for End-to-end Training
O Plátek, F Jur?í?ek – workshop.colips.org
… Plátek, O., B?lohlávek, P., Hude?ek, V., Jur?í?ek, F.: Recurrent neural networks for dialogue state tracking. arXiv preprint arXiv:1606.08733 (2016) 11. Raux, A., Langner, B., Bohus, D., Black, AW, Eskenazi, M.: Let’s go public! Taking a spoken dialog system to the real world. …

Minimization of Regression and Ranking Losses with Shallow Neural Networks on Automatic Sincerity Evaluation
HS Lee, Y Tsao, CC Lee, HM Wang, WC Lin… – Interspeech …, 2016 – iis.sinica.edu.tw
… 2, pp. 203–223, 2011. [5] J. Tepperman, DR Traum, and S. Narayanan, “”Yeah right”: Sarcasm recognition for spoken dialogue systems,” in Proc. Inter- speech Conf., 2006. … 979–988. [18] PL Bartlett, “The sample complexity of pattern classification with neural networks: The size …

An Attentional Neural Conversation Model with Improved Specificity
K Yao, B Peng, G Zweig, KF Wong – arXiv preprint arXiv:1606.01292, 2016 – arxiv.org
… In recent years, neural network based conversa- tion models (Serban et al., 2015b; Sordoni et al., 2015b; Vinyals and Le, 2015; Shang et al., 2015) have emerged as a promising complement to tradi- tional partially observable Markov decision process (POMDP) models (Young …

USTC at NTCIR-12 STC Task
J Zhang, J Hou, S Zhang, L Dai – research.nii.ac.jp
… To build a traditional dialogue system which contains several components[1], a lot of related technologies have been de- veloped such as dialogue … Meanwhile a very popular approach recently developed is to train end-to-end models with re- current neural network on a large …

Many-to-many voice conversion using hidden Markov model-based speech recognition and synthesis
Y Aizawa, M Kato, T Kosaka – The Journal of the Acoustical Society …, 2016 – asa.scitation.org
… Dialectal alarm words recognition based on a hybrid model of Hidden Markov Models and the BP Neural NetworkLing Lu, Xiangyang … Aug 1997. Hidden Markov model?based speech synthesis as a tool for constructing comunicative spoken dialog systemsKeiichi Tokuda Nov …

Multi-label Topic Classification of Turkish Sentences Using Cascaded Approach for Dialog Management System
G So?anc?o?lu, B Köro?lu, O A??n – 2016 – tsdconference.org
… 1. Arora, S., Batra, K., Singh, S.: Dialogue system: A brief review. arXiv preprint arXiv:1306.4134 (2013) 2. Chen, L., Zhang, D., Mark, L.: Understanding user intent in community question answering. … In: Third Turkish Symp. Artificial Intelligence and Neural Networks, Ankara. …

Bootstrapping incremental dialogue systems: using linguistic knowledge to learn from minimal data
D Kalatzis, A Eshghi, O Lemon – arXiv preprint arXiv:1612.00347, 2016 – arxiv.org
… arXiv, (1506.05869v3), 2015. [12] Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke, and Steve Young. Multi-domain neural network language generation for spoken dialogue systems. In Proc. NAACL, 2016. …

“Text, Speech, and Dialogue: 19th International Conference, TSD 2016, Brno, Czech Republic, September 12-16, 2016, Proceedings”
P Sojka, A Horák, I Kope?ek, K Pala – 2016 – books.google.com
… 435 Kim Berninger, Jannis Hoppe, and Benjamin Milde Training Maxout Neural Networks for Speech Recognition Tasks … Asgari, Allison Sliter, and Jan Van Santen Platon: Dialog Management and Rapid Prototyping for Multilingual Multi-user Dialog Systems….. …

Speech Based Access of Agricultural Dealers Information in Odia Language
SG Dontamsetti, PK Sahu – Application and Theory of Computer …, 2016 – archyworld.com
… VoicePedia, a telephone-based dialog system for searching and browsing Wikipedia implemented in [10]. … LDA and MLLT are also used which can be derived from MFCCs features. Improved feature processing can be done using LDA and MLLT for Deep neural networks [24]. …

Hybrid Intelligence and the Future of Work
E Kamar, WA Redmond – research.microsoft.com
… Computational and algorithmic advances in machine learning methods including deep neural networks have led to a new wave of excitement … platforms can be instrumental for the high coverage evaluation of multi-turn interactive systems, such as dialog systems, which cannot …

Automatic Correction of ASR outputs by Using Machine Translation
LF D’Haro, RE Banchs – 2016 – researchgate.net
… Which ASR should I choose for my dialogue system? Proc. SIGDIAL, August. [8] Jaitly, N., Nguyen, P., Senior, AW, & Vanhoucke, V. (2012, September). Application of Pretrained Deep Neural Networks to Large Vocabulary Speech Recognition. In INTERSPEECH (pp. …

Towards a Model for Personality-Based Agents for Emotional Responses
RG Rodrigues, G Paiva Guedes… – Proceedings of the 22nd …, 2016 – dl.acm.org
… Neural networks cartridges for data mining on time series. In International Joint Conference on Neural Networks, 2009, pages 2302–2309, June 2009. … Extraction of affective components from texts and their use in natural language dialogue systems. …

Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features
L Tian, J Moore, C Lai – Spoken Language Technology …, 2016 – ieeexplore.ieee.org
… emotional interaction modules in dialogue systems have been devel- oped with appraisal-based emotion models, and our goal is to build emotion … The LSTM model is a recurrent neural network with multiple hid- den layers and a special structure called “the memory cell” that …

Knowledge-based Framework for Intelligent Emotion Recognition in Spontaneous Speech
R Chakraborty, M Pandharipande… – Procedia Computer …, 2016 – Elsevier
… [5]; V. Petrushin; Emotion in speech: Recognition and application to call centers. In: Artificial Neural Networks in Engineering (ANNIE). (1999), pp. 7–10. … Dialogue Systems Technology and Design; chap. Salient Features for Anger Recognition in German and English IVR Portals. …

Why Blow Out?
R Searle, M Bingham-Walker – eccentricdata.com
… Use of dynamic graphical model as an REPRESENTATION layer working in tandem with a neural network, which acts as a CONTEXTUALIZATION layer. … Weston. Evaluating Prerequisite Qualities for Learning End- to-End Dialog Systems. In 2016 International Conference …

Highlighting Psychological Features for Predicting Child Interjections During Story Telling
G Lejeune, F Rioult, B Crémilleux – Interspeech 2016, 2016 – researchgate.net
… 8604–8608. [15] P. Su, D. Vandyke, M. Gasic, D. Kim, N. Mrksic, T. Wen, and SJ Young, “Learning from real users: Rating di- alogue success with neural networks for reinforcement learning in spoken dialogue systems,” CoRR, 2015. …

Constructing a Natural Language Inference Dataset using Generative Neural Networks
J Starc, D Mladeni? – arXiv preprint arXiv:1607.06025, 2016 – arxiv.org
… 2015) and conversational dialogue systems (Serban et al., 2016a; Banchs and Li, 2012). Another recently popular task is generating captions from images (Vinyals et al., 2015; Socher et al., 2014). With the advancement of deep learning, many neural network approaches have …

Transfer Reinforcement Learning with Shared Dynamics
R Laroche, M Barlier – 2016 – researchgate.net
… Transfer learning for user adaptation in spoken dialogue systems. In Proceedings of the 15th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS). … Curiosity-driven exploration in deep reinforcement learning via bayesian neural networks. …

Computing Feelings
N Tosa – Cross-Cultural Computing: An Artist’s Journey, 2016 – Springer
… 2.2.10 Emotion Recognition Engine Between Different Cultures. We should produce ‘supervisor data’ localized for each country for this type of dialogue system. Tuning is required to make the neural network learn emotional expressions for each country. …

ANITA LILLA VERO,
,MS Grounding – cl.cam.ac.uk,
,”… based representation of phrases towards a goal-oriented, symbol-based dialogue system. Reference: Dr. Daniel Sonntag · Daniel.Sonntag@dfki.de 2012–2015 Researcher, Neural Information Processing Group Neural Information Processing Group • Neural network model …

Encoder-decoder with Focus-mechanism for Sequence Labelling Based Spoken Language Understanding
S Zhu, K Yu – arXiv preprint arXiv:1608.02097, 2016 – arxiv.org
… In spoken dialogue system, the Spoken Language Under- standing (SLU) is a key component that parses user utter- ances into … by a number of very successful continuous-space, neu- ral network and deep learning approaches [5, 6], many neural network architectures have …

The DialPort Portal: Grouping Diverse Types of Spoken Dialog Systems
T Zhao, K Lee, M Eskenazi – workshop.colips.org
… In: Natural Language Dialog Systems and Intelligent Assistants, pp. 53–61. Springer (2015) 15. Sutskever, I., Vinyals, O., Le, QV: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems. pp. 3104–3112 (2014) 16. …

Semantic language models with deep neural networks
AO Bayer, G Riccardi – Computer Speech & Language, 2016 – Elsevier
… Neural network LMs (NNLMs) are first introduced in Bengio et al. (2003). … This may lead to problems especially for spoken dialog systems, where one of the main goals of these systems is to extract user intentions and the meaning of utterances. …

Where Speech Recognition Is Going: Conclusion and Future Scope
S Johar – Emotion, Affect and Personality in Speech, 2016 – Springer
… human aural and spectrogram comparisons, to simple template matching, dynamic time-warping and more modern statistical pattern recognition approaches, such as neural networks and Hidden … Cambridge University Press, Cambridge. 6. Carlson R (2002) Dialogue system. …

Domain adaptation of a speech translation system for lectures by utilizing frequently appearing parallel phrases in-domain
N Goto, K Yamamoto… – Signal and Information …, 2016 – ieeexplore.ieee.org
… lectures into Japanese consisting of an English automatic speech recog- nition system (ASR) that utilizes a deep neural network (DNN) and an … Lectures tend to have much broader topics than such speech translation tasks as spoken dialog systems for travel assistance [2]. The …

Designing Regularizers and Architectures for Recurrent Neural Networks,
,D Krueger – 2016 – papyrus.bib.umontreal.ca,
,”Page 1. Université de Montréal Designing Regularizers and Architectures for Recurrent Neural Networks par David Krueger … tionist methods. Recurrent neural networks are a set of increasingly popular sequential models capable in principle of learning arbitrary algorithms. …

Optimising The Input Window Alignment in CD-DNN Based Phoneme Recognition for Low Latency Processing
AK Dhaka, G Salvi – arXiv preprint arXiv:1606.09163, 2016 – arxiv.org
… neural networks (ANNs) in automatic speech recog- nition (ASR) as well as in many other fields (see [1, 2] for extensive reviews). A key factor that determines the usability of applications based on speech recognition is the latency or lag of the system. In dialogue systems, eg …

Gated End-to-End Memory Networks
F Liu, J Perez – 217.109.185.161
… Shortcut connections have been studied from both the theoretical and practical point of view in the general context of neural network architectures (Bishop, 1995; Ripley … This dataset essentially tests the capacity of end-to- end dialog systems to conduct dialog with various goals. …

Neural Paraphrase Generation with Stacked Residual LSTM Networks
A Prakash, SA Hasan, K Lee, V Datla, A Qadir… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Vinyals et al., 2015), and dialogue systems (Serban et al., 2016). Although paraphrase generation can be formulated as a sequence to sequence learning task, not much work has been done in this area with regard to applica- tions of state-of-the-art deep neural networks. …

Sociocognitive Language Processing–Emphasising the Soft Factors
B Schuller, M McTear – … Workshop on Spoken Dialogue Systems ( …, 2016 – fim.uni-passau.de
… 1. Allen, J., Manshadi, M., Dzikovska, M., Swift, M.: Deep linguistic processing for spoken dialogue systems. … Mesnil, G., Dauphin, Y., Yao, K., Bengio, Y., Deng, L., Hakkani-Tur, D., He, X., Heck, L., Tur, G., Yu, D., Zweig, G.: Using recurrent neural networks for slot filling in spoken …

Assisting Discussion Forum Users using Deep Recurrent Neural Networks
J Suorra Hagstedt P, O Mogren – Proceedings of the …, 2016 – publications.lib.chalmers.se
… Courville, and Joelle Pineau. 2016. Building end-to-end dialogue systems using gener- ative hierarchical neural network models. In Dale Schuurmans and Michael P. Wellman, editors, AAAI, pages 3776–3784. AAAI Press. …

GTM-UVigo System for Albayzin 2016 Speaker Diarisation Evaluation
P Lopez-Otero, L Docio-Fernandez, C Garcia-Mateo – researchgate.net
… References 1. Bishop, CM: Neural Networks for Pattern Recognition. … In: Proceedings of ICASSP. pp. 2494–2498 (2014) 6. Jur?i?ek, F., Dušek, A., Plátek, O., Žilka, L.: Alex: a statistical dialogue systems framework. Lecture Notes in Computer Science 8655, 587–594 (2014) …

Personalizing a Dialogue System with Transfer Learning
K Mo, S Li, Y Zhang, J Li, Q Yang – arXiv preprint arXiv:1610.02891, 2016 – arxiv.org
Page 1. arXiv:1610.02891v2 [cs.AI] 17 Oct 2016 Personalizing a Dialogue System with Transfer Learning … Abstract It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is of- ten insufficient. …

EPAS: Artificial Intelligent System for Assistance
G Tascini – Towards a Post-Bertalanffy Systemics, 2016 – Springer
… 5. Singh, S., Kearns, M., Litman, D., & Walker, M. (2000). Reinforcement learning for spoken dialogue systems. In NIPS, 2000. 6. Tascini, G., Monteanto, A., & Palombo, R. (2000). … Robot localization using incremental neural networks. In Convegno AI*IA04, Perugia, 2004. 17. …

A novel density-based clustering method using word embedding features for dialogue intention recognition
J Jang, Y Lee, S Lee, D Shin, D Kim, H Rim – Cluster Computing, 2016 – Springer
… [11] proposed an ensemble classification method for a Korean online messenger dialogue system; this method … Recently, vector learning methods that use neural network-based language models have achieved high performance in natural language understanding tasks [22, 23 …

Recent Advances on Human-Computer Dialogues
X Wang, C Yuan – CAAI Transactions on Intelligence Technology, 2016 – Elsevier
… Recent years, with the help of rapidly increasing computational capability, more dialogue corpora publicly available as well as some new technologies like deep neural networks, there are also some new trends in the developments of dialogue systems. …

Chatterbot for Education: a Study based on Formal Concept Analysis for Instructional Material Recommendation
S Moraes, R Machado – Brazilian Symposium on Computers in Education ( …, 2016 – br-ie.org
… stimulus-response dialog systems. 1348 … In our study, we are using texts in Portuguese about Artificial Intelligence (AI). These texts are already classified by topic (eg: agents, neural networks, knowledge representation, etc.). …

ASR Error Management Using RNN Based Syllable Prediction for Spoken Dialog Applications
B Kim, J Choi, GG Lee – Advances in Parallel and Distributed Computing …, 2016 – Springer
… In: International workshop series on spoken dialogue systems technology (IWSDS). 4. Robinson T, Hochberg M, Renals S (1996) The use of recurrent neural networks in continuous speech recognition. In: Automatic speech and speaker recognition. …

BI-DIRECTIONAL SPEECH TO TEXT CONVERSION WITH BLIND SOURCE SEPARATION AND SPECTRAL SUBTRACTION
RM Deepika, NLS Chavakula, NV Posilakshmi – ijeec.com
… called \Communicator”” which aims to provide a more natural dialog system for travel reservations as compared to current commercial products. A key … conversion by using recurrent neural networks trained with the real time recurrent learning (RTRL) algorithm. …

Computing Stories
N Tosa – Cross-Cultural Computing: An Artist’s Journey, 2016 – Springer
… The simplest dialogue system with which the computer and the user alternatively speak to compose phrases of a predetermined poem is illustrated … As the basic architecture for emotion recognition, I used the neural network first introduced in “Neuro-Baby” as described in Chap. …

A low cost personalised robot language tutor with perceptual and interaction capabilities
M Madhyastha, DB Jayagopi – India Conference (INDICON), …, 2016 – ieeexplore.ieee.org
… limited with the exception of some of the work by Dan Bohus [2] [3]. Bohus et all have come up with an open dialogue system in which … Deep-convolutional neural networks (CNN) are used for classifying age and gender and the models have been trained using Caffe [14] [15]. …

Coherent Dialogue with Attention-based Language Models
H Mei, M Bansal, MR Walter – arXiv preprint arXiv:1611.06997, 2016 – arxiv.org
… (2016) improve spoken dialog systems via multi-domain and semantically conditioned neural networks on dialog act rep- resentations and explicit slot-value formulations. Our work explores the ability of recurrent neural network language models (Bengio et al. …

Globally Coherent Text Generation with Neural Checklist Models,
,CKLZY Choi – aclweb.org,
,”… (2015) and Wen et al. (2016) present neural network models for generating dialogue system responses given a set of agenda items. They focus on gener- ating short texts (1-2 sentences) in a relatively small vocabulary setting and assume a fixed set of possi- ble agenda items. …

Topic Augmented Neural Network for Short Text Conversation
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv: …, 2016 – pdfs.semanticscholar.org
Page 1. Topic Augmented Neural Network for Short Text Conversation Yu Wu†? , Wei Wu‡ , Zhoujun Li† , Ming Zhou‡ … To this end, we propose a topic aug- mented neural network which consists of a sentence embedding layer, a topic em- bedding layer, and a matching layer. …

Study on Optimal Spoken Dialogue System for Robust Information Search in the Real World,
,?? – 2016 – eprints.lib.hokudai.ac.jp,
,”… Page 29. Chapter 2. Key Technologies of Spoken Dialogue Systems and Related Works 16 duration of the longest voiced speech. … Recently, HMM and deep neural network (DNN) are also applied [34] [35]. Among the prosody features, pitch information is essential. …

Implementation of image processing based Digital Dactylology Converser for deaf-mute persons
MY Javed, MM Gulzar, STH Rizvi… – Intelligent Systems …, 2016 – ieeexplore.ieee.org
… [4] W. Gao, J. Ma, S. Shan, X. Chen, W. Zeng, H. Zhang, 1. Yan, and J. Wang, “”HandTalker: A multi modal dialog system using sign … [8] C. Oz and MC Leu, “”American Sign Language word recognition with a sensory glove using artificial neural networks,”” Engineering Applications …

Response Selection with Topic Clues for Retrieval-based Chatbots
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1605.00090, 2016 – arxiv.org
… matching between a message and a response is not only conducted between a message vector and a response vector generated by convolutional neural networks, but also … Existing conversation systems include task ori- ented dialog systems and non task oriented chatbots. …

Application Areas of AI Systems
M Flasi?ski – Introduction to Artificial Intelligence, 2016 – Springer
… In case such knowledge is unavailable an approach based on pattern recognition or neural networks can be used. … from a text, automatic summarizing, Optical Character Recognition (OCR), speech synthesis (on the basis of a text), simple question-answer dialogue systems, etc …

Non-sentential Question Resolution using Sequence to Sequence Learning
V Kumar, S Joshi – aclweb.org
… Uni- versity of London. Iulian Serban, Alessandro Sordoni, Yoshua Bengio, Aaron C. Courville, and Joelle Pineau. 2016. Building end- to-end dialogue systems using generative hierarchical neural network models. In AAAI. Ilya Sutskever, Oriol Vinyals, and Quoc V Le. 2014. …

Year of Publication: 2015
SN Kayte, M Mundada, C Kayte – pdfs.semanticscholar.org
… More recent applications include spoken dialogue systems and communicative robots … Sangramsing Kayte, Dr.Bharti Gawali “”The Marathi Text-To-Speech Synthesizer Based On Artificial Neural Networks “” International Research Journal of Engineering and Technology (IRJET) e …

and Language Technologies for Iberian Languages
A Abad, A Ortega, A Teixeira, CG Mateo… – Springer
… Result s…. 108 María Pilar Fernández-Gallego, Álvaro Mesa-Castellanos, Alicia Lozano-Díez, and Doroteo T. Toledano Deep Neural Network-Based Noise … 234 Emilio Granell and Carlos-D. Martínez-Hinarejos Assessing User Expertise in Spoken Dialog System Interactions …

Sentence Level Recurrent Topic Model: Letting Topics Speak for Themselves
F Tian, B Gao, D He, TY Liu – arXiv preprint arXiv:1604.02038, 2016 – arxiv.org
… Different from conventional topic models that largely ignore the sequential order of words or their topic coher- ence, SLRTM gives full characterization to them by using a Recurrent Neural Networks (RNN) based framework. …

Determining speaker attributes from stress-affected speech in emergency situations with hybrid SVM-DNN architecture
J Ahmad, M Sajjad, S Rho, S Kwon, MY Lee… – Multimedia Tools and …, 2016 – Springer
… Deep neural network . … more efficiently [4]. Detecting speaker gender from a brief utterance is a challenging task with rapidly growing applications in communication, human-computer inter- action (HCI), telephone speech forensic analysis, and natural language dialog systems. …

The splab at the NTCIR-12 Short Text Conversation Task
K Wu, X Liu, K Yu – research.nii.ac.jp
… In the subtask, we build a single round of retrival-based dialogue system based on a repository of weibo data, which is provided by the … In the semantic layer, we apply a regular feed-forward fully connected neural network to the fixed-length feature vector to obtain the final …

Manipulating Word Lattices to Incorporate Human Corrections
Y Gaur, F Metze, JP Bigham – Interspeech 2016, 2016 – cs.cmu.edu
… [7] used it to transcribe podcasts, [8] used it to collect data from speech dialog systems and [9, 10 … 8. References [1] G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kings- bury, “Deep neural networks for acoustic …

Architecting an Intelligent Tutoring System with an Affective Dialogue Module
S Jiménez, R Juárez-Ramírez… – Software …, 2016 – ieeexplore.ieee.org
… Most of the dialogue systems reported in literature incorporate techniques of nat- ural language processing and Artificial Intelligence … index using decision-making techniques or knowledge- based systems such as Bayesian Networks, Fuzzy-Logic, Neural Networks, Naive Bayes …

Recurrent Memory Addressing for describing videos
KK Agrawal, AK Jain, A Agarwalla, P Mitra – arXiv preprint arXiv: …, 2016 – arxiv.org
… pabitra}@iitkgp.ac.in Abstract Deep Neural Network architectures with external mem- ory components allow the model to perform inference and capture long term dependencies, by storing information ex- plicitly. In this paper, we …

Multimodal Memory Modelling for Video Captioning
J Wang, W Wang, Y Huang, L Wang, T Tan – arXiv preprint arXiv: …, 2016 – arxiv.org
… propose a Neural Turing Machine (NTM) which holds an external memory to interact with the internal state of neural networks by attention … need long-term dependency modelling, eg, textual ques- tion answering [3, 14], visual question answering [39] and dialog systems [8]. As …

Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks
G Zhou, Z Zeng, JX Huang, T He – … of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… Classification with Weakly Shared Deep Neural Networks … To this end, we propose a novel DNNs that hierarchically learns to transfer the semantic knowledge from the source language to the target language, namely Weakly Shared Deep Neural Networks (WSDNNs). …

“KSR COLLEGE OF ENGINEERING,(Autonomous)”,
,S No – ksrce.ac.in,
,”Page 1. ME – Computer Science and Engineering KSRCE – Curriculum and Syllabi (R 2016) 1 KSR COLLEGE OF ENGINEERING (Autonomous) (Approved by AICTE& Affiliated to Anna University) KSR Kalvi Nagar, Tiruchengode – 637 215 CURRICULUM PG R – 2016 …

A Web-based Platform for Collection of Human-Chatbot Interactions
L Lin, LF D’Haro, R Banchs – … of the Fourth International Conference on …, 2016 – dl.acm.org
… https://developer.amazon.com/alexa. 2. Rafael E. Banchs, Haizhou Li. IRIS: a Chat-oriented Dialogue System based on the Vector Space Model. 2012. … A neural network approach to context-sensitive generation of conversational responses. arXiv:1506.06714. 2015. …

Coupling Distributed and Symbolic Execution for Natural Language Queries
L Mou, Z Lu, H Li, Z Jin – arXiv preprint arXiv:1612.02741, 2016 – arxiv.org
… MarcAurelio Ranzato, Sumit Chopra, Michael Auli, and Woj- ciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016. … A network-based end-to-end trainable task- oriented dialogue system. arXiv preprint arXiv:1604.04562, 2016. …

1 Adaptability: Trainable end-to-end Natural Language Generation systems,
,I Konstas – ikonstas.net,
,”… In contrast, for CODE-NN and AMR generation I used neural network architectures, which usually train in linear time and take advantage … Existing dialogue systems currently fall under two extremes: (a) they are either confined to very small domains, eg, booking hotels or giving …

The aNALoGuE Challenge: Non Aligned Language GEneration
J Novikova, V Rieser – The 9th International Natural Language …, 2016 – aclweb.org
… We predict that longer tar- get outputs are challenging for, eg neural networks due to the vanishing gradient problem. … Amanda Stent, Rashmi Prasad, and Marilyn Walker. 2004. Trainable sentence planning for complex infor- mation presentation in spoken dialog systems. …

1 Completed Research,
,N Jiang – eecs.umich.edu,
,”… It provides a general and unified framework that captures many important AI applications, including dialog systems, self-driving cars, robots for … difficulty stated in (a) can be overcome by deploying powerful function approximation techniques, such as deep neural networks [1, 2 …

“Kalitzin, SN, Bauer, PR, Lamberts, RJ, Velis, DN & Thijs, RD, Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal …”
S Knieling, KS Sridharan, P Belardinelli, G Naros… – World Scientific
… Griol, D., Iglesias, JA, Ledezma, A. & Sanchis, A., A Two-Stage Combining Classifier Model for the Development of Adaptive Dialog Systems … Peláez, FJR, Aguiar-Furucho, MA & Andina, D., Intrinsic Plasticity for Natural Competition in Koniocortex-Like Neural Networks …

Generating Paraphrases from DBPedia using Deep Learning
A Sleimi, C Gardent – WebNLG 2016, 2016 – webnlg2016.sciencesconf.org
… 2011. Generating text with recurrent neural networks. In Proceedings of the 28th International Conference on Machine Learning (ICML-11), pages 1017–1024. … 2015. Semantically conditioned ltsm-base natural lan- guage generation for spoken dialogue systems. …

Information Fusion in Automatic User Satisfaction Analysis in Call Center
J Sun, W Xu, Y Yan, C Wang, Z Ren… – … (IHMSC), 2016 8th …, 2016 – ieeexplore.ieee.org
… data with a human-machine spoken dialogue is adopted in Callejas and Griol’s [2] work, which presents a method for automatic prediction of the user’s mental states in a human-machine spoken dialogue system. … We can try to use more efficient classifier such as neural network. …

Ranking Responses Oriented to Conversational Relevance in Chat-bots
B Wu, B Wang, H Xue – aclweb.org
… Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2015. Building end-to- end dialogue systems using generative hierarchical neural network models. arXiv preprint arXiv:1507.04808. Lifeng Shang, Zhengdong Lu, and Hang Li. 2015. …

Neural Document Embeddings for Intensive Care Patient Mortality Prediction
P Grnarova, F Schmidt, SL Hyland… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Following recent work in document classification [21] and dialogue systems [17], we adopt a two- layer architecture. … For both levels we use convolutional neural networks (CNNs) with max-pooling which have shown excellent results on binary text classi- fication tasks [7], [18]. …

Predicting Dialogue Acts for Intelligent Virtual Agents with Multimodal Student Interaction Data
W Min, AK Vail, MH Frankosky, JB Wiggins, KE Boyer… – pdfs.semanticscholar.org
… The selection of dialogue moves was informed by the literature on dialogue systems for learning [8], as well as experience with a recent … 5.1 LSTM Background LSTMs are a type of gated recurrent neural network specifically designed for sequence labeling on temporal data. …

The NITech text-to-speech system for the Blizzard Challenge 2016
K Sawada, C Asai, K Hashimoto, K Oura, K Tokuda – festvox.org
… and such systems are now used in various applica- tions, such as for in-car navigation, smartphones, and spoken dialogue systems. … SPSS, eg, involving hidden Markov model- [2] and deep neural network (DNN)-based speech syn- thesis [3], has been actively investigated and …

Dialogues with Social Robots
K Jokinen, G Wilcock – Springer
… Stefan Ultes, Alexander Schmitt and Wolfgang Minker Recurrent Neural Network Interaction Quality Estimation….. 381 Louisa Pragst, Stefan Ultes and Wolfgang Minker An Evaluation Method for System Response in Chat-Oriented Dialogue System….. …

Voice Dialogue with a Collaborative Robot Driven by Multimodal Semantics
A Kharlamov, K Ermishin – International Conference on Interactive …, 2016 – Springer
… under the project Study of the mechanism of associative links in human verbal and cogitative activity using the method of neural network modeling in … Hum. 41(3), 492–509 (2011)CrossRef. 2. Heriberto, C., Nina, D., Kai-Florian, R., Thora, T., John, B.: A dialogue system for indoor …

Training an Interactive Humanoid Robot Using Multimodal Deep Reinforcement Learning
H Cuayáhuitl, G Couly, C Olalainty – arXiv preprint arXiv:1611.08666, 2016 – arxiv.org
… [2] JA Boyan, JA Boyan, JA Boyan, and JA Boyan. Modular neural networks for learning context- dependent game strategies. Master’s thesis, 1992. … [4] H. Cuayáhuitl. SimpleDS: A simple deep reinforcement learning dialogue system. CoRR, abs/1601.04574, 2016. …

Shall I Be Your Chat Companion?: Towards an Online Human-Computer Conversation System
R Yan, Y Song, X Zhou, H Wu – … of the 25th ACM International on …, 2016 – dl.acm.org
… Recently, with the fast development of deep learning techniques, efforts are devoted in the neural network-based conversation sys- tems. … A hierarchical neural network model is proposed to model human conversations [28]. …

Neural text generation from structured data with application to the biography domain
R Lebret, D Grangier, M Auli – arXiv preprint arXiv:1603.07771, 2016 – arxiv.org
… (2016) who use an encoder-decoder style neural network model to tackle the … This mechanism is inspired by recent work on attention based word copying for neural machine translation (Luong et al., 2015) as well as delexicalization for neural dialog systems (Wen et al., 2015). …

Large-Scale Acquisition of Commonsense Knowledge via a Quiz Game on a Dialogue System
N Otani, D Kawahara, S Kurohashi, N Kaji… – OKBQA …, 2016 – pdfs.semanticscholar.org
… 2016. Design of word association games using dialog systems for acquisition of word association knowledge. In … 2015. Morphological analysis for unsegmented lan- guages using recurrent neural network language model. In …

An Empirical Investigation of Word Clustering Techniques for Natural Language Understanding
DA Shunmugam, P Archana – International Journal of Engineering …, 2016 – ijesc.org
… Turian et.al.uses neural networks in a non-probabilistic language modeling framework. … The first set is internally collected multimedia data from live deployment scenarios of a spoken dialog system designed for entertainment search for Xbox One game console. …

Dialog Management
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… parameters. The algorithms were evaluated on a statistical dialog system for the tourist information domain modeled as a POMDP. The … state). Wierstra et al. (2010) used recurrent neural networks (RNN) to approximate the policy. This …

Detecting Context Dependent Messages in a Conversational Environment
C Li, Y Wu, W Wu, C Xing, Z Li, M Zhou – arXiv preprint arXiv:1611.00483, 2016 – arxiv.org
… [Serban et al.2015] Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. 2015. Building end-to-end dialogue systems using generative hierarchical neural network models. arXiv preprint arXiv:1507.04808. …

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting and Tracking Popular Discussion Threads
J He, M Ostendorf, X He, J Chen… – arXiv preprint arXiv …, 2016 – pdfs.semanticscholar.org
… Early work such as TD-gammon used a neural network to approxi- mate the state value function (Tesauro, 1995). … In natu- ral language processing, reinforcement learning has been applied successfully to dialogue systems that generate natural language and converse with a hu …

Configuration and evaluation of a constrained nutrition dialogue system,
,E Tuan – 2016 – dspace.mit.edu,
,”Page 1. Configuration and Evaluation of a Constrained Nutrition Dialogue System by Eann Tuan … application in the medical domain. 1.2 Current Dialogue Systems Voice controlled personal assistants have become increasingly widespread. From Siri …

Recent improvements on error detection for automatic speech recognition
Y Esteve, S Ghannay… – … on Multimodal Media …, 2016 – pdfs.semanticscholar.org
… and semantic features for theme identification in telephone conversations’, in 6th International Work- shop on Spoken Dialog Systems (IWSDS 2015 … [20] Yik-Cheung Tam, Yun Lei, Jing Zheng, and Wen Wang, ‘Asr error de- tection using recurrent neural network language model …

GuessWhat?! Visual object discovery through multi-modal dialogue
H de Vries, F Strub, S Chandar, O Pietquin… – arXiv preprint arXiv: …, 2016 – arxiv.org
… introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. … Thanks to advances in training deep neural networks [16] and the availability of large-scale classification datasets [26, 35, 49], automatic …

Second International Afro-European Conference for Industrial Advancement AECIA 2015
A Abraham, K Wegrzyn-Wolska, AE Hassanien… – Springer
… Windows….. 185 Hadhami Kaabi Solar Power Production Forecasting Based on Recurrent Neural Network….. 195 … Contents Page 14. Dialogue Systems: Modeling and Prediction of their Dynamics….. 421 …

Human-like Natural Language Generation Using Monte Carlo Tree Search
KKI Kobayashi, D Mochihashi… – The INLG 2016 … – webprojects.eecs.qmul.ac.uk
… V. Rieser and O. Lemon. 2009. Natural language genera- tion as planning under uncertainty for spoken dialogue systems. In EACL 2009, pages 683–691. … 2016. Mastering the game of go with deep neural networks and tree search. Nature, 529: 484–489. …

Continuous Wave Interference Effects on Global Position System Signal Quality
F Ye, H Yu, Y Li – Computer, 2016 – waset.org”,
,Conferences.,

International Conference on Oriental Thinking and Fuzzy Logic
BY Cao, PZ Wang, ZL Liu, YB Zhong – Springer
… 415 Pei-Hua Wang, Hai-Tao Lin and Xiao-Peng Yang Part IV Factor Space and Factorial Neural Networks Factor Space and Normal Context on C oncept … 687 Ruihua Geng Multi-sentence Level Natural Language Generation for Dialogue System….. …

Unsupervised Dialogue Act Induction using Gaussian Mixtures
T Brychcín, P Král – arXiv preprint arXiv:1612.06572, 2016 – arxiv.org
… Automatic DA recognition is fundamental for many applications, starting with dialogue systems (Allen et al., 2007). The expansion of social media in the last years has led to many other interesting … 2013. Recurrent convolutional neural networks for discourse compositionality. …

Implementing Deep Learning Object Recognition on NAO,
,Y Philippczyk – 2016 – hdms.bsz-bw.de,
,”… ment a ready-to-use object recognition implementation on the NAO robotic platform using Convolutional Neural Networks based on pretrained models. Recognition of multiple objects … to easier acquire objects in the field of view. Additionally, a dialogue system for querying …

Recent Advances in Nonlinear Speech Processing: Directions and Challenges
A Esposito, M Faundez-Zanuy, AM Esposito… – Recent Advances in …, 2016 – Springer
… Kluwer, Dordrecht (1990)CrossRef. 24. Meena, R., Skantze, G., Gustafson, J.: Data-driven models for timing feedback responses in a map task dialogue system. Comput. Speech Lang. … In: Bassis et al. (eds.) Advances in Neural Networks: Computational and Theoretical Issues. …

WORD REPRESENTATION USING ADeep NEURAL NETWORK,
,Y Li – 2016 – tspace.library.utoronto.ca,
,”Page 1. WORD REPRESENTATION USING ADEEP NEURAL NETWORK by Yunpeng Li … Page 2. Abstract Word Representation Using A Deep Neural Network Yunpeng Li Master of Information Graduate Department of Faculty of Information University of Toronto 2016 …

Content Selection in Data-to-Text Systems: A Survey
D Gkatzia – arXiv preprint arXiv:1610.08375, 2016 – arxiv.org
… In this framework, content features are seen as a hypergraph where nodes denote words. Graphical models have been used for NLG in dialogue systems as well, eg (Dethlefs and Cuayahuitl, 2011). … Sowdaboina et al (2014) use neural networks for Page 10. …

Improvements in IITG Assamese Spoken Query System: Background Noise Suppression and Alternate Acoustic Modeling
S Shahnawazuddin, D Thotappa, A Dey… – Journal of Signal …, 2016 – Springer
… feature extraction. In addition to this, we have also explored the recently reported subspace Gaus- sian mixture (SGMM) and deep neural network (DNN) S. Shahnawazuddin … model parameters. 4 Deep Neural Network The GMM …

A Proposal for Evaluating Answer Distillation from Web Data
B Mitra, G Simon, J Gao, N Craswell, L Deng – plg2.cs.uwaterloo.ca
… How not to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. arXiv preprint arXiv:1603.08023, 2016. … A neural network approach to context-sensitive generation of conversational responses. 2015. …

Evolvable dialogue state tracking for statistical dialogue management
K Yu, L Chen, K Sun, Q Xie, S Zhu – Frontiers of Computer Science, 2016 – Springer
… in SLU, such as recurrent neural net- works (RNN) [20], long short-term memory (LSTM) neural networks [21] and recursive neural networks (RecNNs) [22 … Meanwhile, in a conversational dialogue system, not only the output of ASR, but also the history of dialogue can be used in …

The Use of Semantic Word Classes in Document Classification
S Ostrogonac, B Popovi?, M Se?ujski – researchgate.net
… Science and Technological Development of the Republic of Serbia, within the project TR32035: “Development of Dialogue Systems for Serbian and … Tomáš Mikolov, “Statistical language models based on neural networks”, in PhD Thesis, Brno University of Technology, 2012. …

Predicting Emotional Responses From Spontaneous Social-Affective Interaction Data
N Lubis, S Sakti, G Neubig, K Yoshino, S Nakamura – phontron.com
… After reducing the dimensions, we train a neural network classifier with one hidden layer using Theano and the PDNN toolkit. … Our experiment on automatic predic- tion offers an approach in equipping conversational agents and dialogue systems with social-affective awareness. …

SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
S Mehri, K Kumar, I Gulrajani, R Kumar, S Jain… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Iulian V Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models. In Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI-16), 2016. …

Combating Reinforcement Learning’s Sisyphean Curse with Intrinsic Fear
ZC Lipton, J Gao, L Li, J Chen, L Deng – arXiv preprint arXiv:1611.01211, 2016 – arxiv.org
… Some investigated applications include robotics (Levine, 2016), dialogue systems (Fatemi et al., 2016; Lipton et al., 2016), energy management (Night, 2016), and self-driving cars (Shalev … When training a DQN, we successively update a neural network based on experiences. …

Dialogue manager domain adaptation using Gaussian process reinforcement learning
M Gaši?, N Mrkši?, LM Rojas-Barahona, PH Su… – Computer Speech & …, 2016 – Elsevier
… Abstract. Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. … 1. Introduction. Spoken dialogue systems allow humans to interact with machines using natural speech. As such, they have many benefits. …

Efficient Acoustic Front-End Processing for Tamil Speech Recognition using Modified GFCC Features
C Vimala, V Radha – … Journal of Image, Graphics and Signal …, 2016 – search.proquest.com
… It has been applied in many research areas like dictation, dialog system and voice based information search etc. … However, for Tamil Speech Recognition most of the experiments are based on conventional MFCC and LPC features with HMM and Neural Networks (NN). …

Leveraging Sentence-level Information with Encoder LSTM for Semantic Slot Filling
G Kurata, B Xiang, B Zhou, M Yu – arXiv preprint arXiv:1601.01530, 2016 – arxiv.org
… [Liu and Lane2015] Bing Liu and Ian Lane. 2015. Re- current neural network structured output prediction for spoken language understanding. In Proc. … 2013a. Asgard: A portable architecture for multilingual dialogue systems. In Proc. ICASSP, pages 8386–8390. …

Modeling Satire in English Text for Automatic Detection
AN Reganti, T Maheshwari, U Kumar… – … (ICDMW), 2016 IEEE …, 2016 – ieeexplore.ieee.org
Page 1. Modeling Satire in English Text for Automatic Detection Aishwarya N Reganti, Tushar Maheshwari, Upendra Kumar, Amitava Das IIIT, Sri City, Chittoor, India {aishwarya.r14,tushar.m14, upendra.k14,amitava.das}@iiits.in …

Implementing Dialog Management
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… Weka) Software. 13 Detailed documentation about this software can be found at here. 14 Different classifier functions can be selected using the software (artificial neural networks, decision trees, Naïve Bayes, etc.). You can …

The Roberta IRONSIDE project A cognitive and physical robot coach for dependent persons
H Sansen, G Chollet, C Glackin, K Jokinen… – Handicap 2016, …, 2016 – iieta.org
… Fully Automatic Speech Processing Techniques for Interactive Voice Servers, Speech Processing, Recognition and Artificial Neural Networks, 1999, Proceedings of the 3rd … Interaction with Robots, Knowbots and Smartphones – Putting Spoken Dialog Systems into Practice. …

A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition
CP Latha, M Priya – APTIKOM Journal on Computer Science …, 2016 – jurnal.aptikom.or.id
… The term “deep learning”” indicates the method used in training multi-layered neural networks. … Keywords: Deep Learning; Facial Electromyography; Emotions; Deep Boltmann Machine; Deep Belief Networks; Convolutional Neural Networks; Stacked Auto Encoders …

RNN-based Encoder-decoder Approach with Word Frequency Estimation
J Suzuki, M Nagata – arXiv preprint arXiv:1701.00138, 2016 – arxiv.org
… [2014], Cho et al. [2014] question answering Xu et al. [2015], dialogue system Vinyals and Le [2015], Shang et al. … Sequence to Sequence Learning with Neural Networks. In Advances in Neural Information Processing Systems 27 (NIPS 2014), pages 3104–3112, 2014. …

Question Similarity Modeling with Bidirectional Long Short-Term Memory Neural Network
C An, J Huang, S Chang… – Data Science in …, 2016 – ieeexplore.ieee.org
… [25] Yin W, Schütze H. Convolutional neural network for paraphrase identification[C … 2015: 901-911. [26] Wen TH, Gasic M, Mrksic N, et al. Semantically conditioned lstm-based natural language generation for spoken dialogue systems[J]. arXiv preprint arXiv:1508.01745, 2015. …

Task Lineages: Dialog State Tracking for Flexible Interaction
S Lee, A Stent – 17th Annual Meeting of the Special Interest Group on …, 2016 – aclweb.org
… A task state is analogous to a dialog state in typical dialog systems. … Interestingly, this is a cru- cial reason behind advances in deep neural network models using the attention mechanism (Bahdanau et al., 2014). …

Generalizing Skills with Semi-Supervised Reinforcement Learning
C Finn, T Yu, J Fu, P Abbeel, S Levine – arXiv preprint arXiv:1612.00429, 2016 – arxiv.org
… reward functions are often hard to measure in the real world, es- pecially in domains such as robotics and dialog systems, where the … require control directly from images, and show that our approach can improve the gener- alization of a learned deep neural network policy by …

Cross-corpus speech emotion recognition based on transfer non-negative matrix factorization
P Song, W Zheng, S Ou, X Zhang, Y Jin, J Liu… – Speech …, 2016 – Elsevier
… With the development of computer technologies, the demands for emotion recognition in new spoken dialogue systems are very urgent. … learning, have been developed to implement the classification function, eg, support vector machine (SVM), neural network (NN), Gaussian …

Institute of Communications Engineering Staff
M Bossert, R Fischer, W Minker, UC Fiebig… – Journal of Siberian …, 2016 – uni-ulm.de
… W. Minker Hierarchical Neural Network Structures for Modeling Inter and Intra Phonetic Information for Phoneme Recognition Springer Verlag, Heidelberg (Germany), 2013 Link to Document Bibtex. T. Heinroth and W. Minker Introducing Spoken Dialogue Systems into Intelligent …

Turn-taking Enhancement in Spoken Dialogue Systems with Reinforcement Learning,
,H Khouzaimi – 2016 – tel.archives-ouvertes.fr,
,”Page 1. Turn-taking Enhancement in Spoken Dialogue Systems with Reinforcement Learning Hatim Khouzaimi To cite this version: Hatim Khouzaimi. … 21 Page 23. Page 24. Chapter 1 Spoken dialogue systems and incremental processing 1.1 Human dialogue 1.1.1 Dialogue acts …

Dialog State Tracking Based on Pairwise Ranking,
,V Miljanic – courses.washington.edu,
,”… Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems. Computer Speech and Language, 24(4), 562–588. … Henderson, M., Thomson, B., & Young, S. (2013). Deep Neural Network Approach for the Dialog State Tracking Challenge. …

“Simplified car simulator usage in hmi research in chosen active safety systems conditions, for semi-autonomous vehicles”
T Dziewo?ski, D Jastrz?bski, M Miros?aw… – Autobusy: technika, …, 2016 – yadda.icm.edu.pl
… tomorrow to the next day” “…” Tests were made in which occupants were to book a hotel only using SDS (speech dialog system). … Kun O., Yung-Ching, L. Risk prediction model for drivers’ in-vehicle activities – Application of task analysis and back- propagation neural network. …

Humans and Machines in the Evolution of AI in Korea
BT Zhang – AI Magazine, 2016 – go.galegroup.com
… research in the early 1990s focused on intelligent control, speech recognition, and handwritten character recognition using neural networks. … The goal is to develop natural language dialogue systems for knowledge communications between humans and machines in specific …

Music Predictions Using Deep Learning. Could LSTM Networks be the New Standard for Collaborative Filtering?
E Keski-Seppälä, M Snellman – 2016 – diva-portal.org
… of a recurrent neural network (RNN) as a recommender system by comparing it to one of the most common recommender system implementations, the matrix factorization method. … “Recurrent Neural Network” (RNN) är bättre på att förutspå vilken musik användare vill lyssna …

Towards Using Conversations with Spoken Dialogue Systems in the Automated Assessment of Non-Native Speakers of English
D Litman, S Young, M Gales, K Knill… – 17th Annual Meeting of …, 2016 – aclweb.org
… International Workshop on Spoken Dialogue Systems. Nikola Mrkšic, Diarmuid O Séaghdha, Blaise Thom- son, Milica Gasic, Pei-Hao Su, David Vandyke, Tsung-Hsien Wen, and Steve Young. 2015. Multi- domain dialog state tracking using recurrent neural networks. …

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads
J He, M Ostendorf, X He, J Chen, J Gao, L Li… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Early work such as TD-gammon used a neural network to approxi- mate the state value function (Tesauro, 1995). … In natu- ral language processing, reinforcement learning has been applied successfully to dialogue systems that generate natural language and converse with a hu …

Towards Empathetic Human-Robot Interactions
P Fung, D Bertero, Y Wan, A Dey, RHY Chan… – arXiv preprint arXiv: …, 2016 – arxiv.org
… It follows that we shall embody interactive dialog systems in simulated or robotic forms. … 5 DNN, CNN and LSTM In this section we give a general description of the Deep Neural Network architec- tures we use in the task described. The real power of any DNN is to reduce a set of …

An approach of a computerized planning assistant to the system design of collaborative robot installations
S Keller, R Hausmann, L Kressner… – … and Factory Automation …, 2016 – ieeexplore.ieee.org
… The 5 different algorithms: Linear Optimisation, Artificial Neural Networks, Decision Trees, Genetic Algorithms and Cased-Based Reasoning were evaluated and the comparative … The dialog system shown in figure 5 is the primary interface between the user and the expert system …

Reference-Aware Language Models
Z Yang, P Blunsom, C Dyer, W Ling – arXiv preprint arXiv:1611.01628, 2016 – arxiv.org
… How should the RE be rendered?) augment a traditional recurrent neural network language model and the two components are combined as a … We first apply our model on task-oriented dialogue systems in the domain of restaurant recommenda- tions, and work on the data set …

Web-Based Automatic Language Identification System
MM Olvera, A Sánchez… – International Journal of …, 2016 – search.proquest.com
… utterance [1]. Some of the most important applications of LID systems consist in multilingual spoken dialog systems, which can … characteristic of a language; and for modeling and classification, several techniques as Markovs models, artificial neural networks, vector quantization …

“Advances in Speech and Language Technologies for Iberian Languages: Third International Conference, IberSPEECH 2016, Lisbon, Portugal, November 23- …”
A Abad, A Ortega, AJ da Silva Teixeira, CG Mateo… – 2016 – books.google.com
… 108 María Pilar Fernández-Gallego, Álvaro Mesa-Castellanos, Alicia Lozano-Díez, and Doroteo T. Toledano Deep Neural Network-Based Noise Estimation for … 234 Emilio Granell and Carlos-D. Martínez-Hinarejos Assessing User Expertise in Spoken Dialog System Interactions …

Interactive Collaborative Robotics and Natural Language Interface Based on Multi-agent Recursive Cognitive Architectures
M Anchokov, V Denisenko, Z Nagoev… – International Conference …, 2016 – Springer
… The system provides a bi-directional exchange of natural language statements in a dialog system style and is designed to input tasks (missions … Anchokov, MI: Solution to the problem of training a multi-agent neural network by means of a multi-chromosome genetic algorithm. …

Asynchronous and Event-based Fusion Systems for Affect Recognition on Naturalistic Data in Comparison to Conventional Approaches
F Lingenfelser, J Wagner, J Deng… – IEEE Transactions …, 2016 – ieeexplore.ieee.org
… Some of the latter algorithms consider temporal alignments between modalities and observed frames by applying asynchronous neural networks that use memory blocks to model temporal dependencies. … Finally, Recurrent Neural Networks (RNNs) offer …

Exploring the Correlation of Pitch Accents and Semantic Slots for Spoken Language Understanding
S Stehwien, NT Vu – Interspeech 2016, 2016 – researchgate.net
… for our ASR system is trained on the training set of the Wall Street Journal (WSJ) corpus [29] using a neural network model setup … The speech files consist of single utter- ances by speakers requesting flight information from a dialog system, for example Show flights from Burbank …

A survey of voice translation methodologies—Acoustic dialect decoder
H Krupakar, K Rajvel, B Bharathi… – Information …, 2016 – ieeexplore.ieee.org
… There are a lot of methods used for ASR including HMMs, DTW, neural networks and deep neural networks[3]. HMMs are used in speech recognition because a speech signal can be visualised as a piecewise stationary signal or a short-time stationary signal. …

Social Affordance Tracking over Time-A Sensorimotor Account of False-Belief Tasks
J Bütepage, H Kjellström, D Kragic – nada.kth.se
… Due to the nature of neural networks, the performance increased with an increasing amount of training samples. … In 7th Interna- tional Workshop on Spoken Dialogue Systems. Friston, K., Mattout, J., & Kilner, J. (2011). Action un- derstanding and active inference. …

Information Extraction from Spoken Diet Records,
,AK Gaddipati – ace.cs.ohiou.edu,
,”… hamper the performance of the overall dialogue system. With the recent advancements in deep learning, Page 8. 8 neural systems that use Convolutional Neural Networks (CNN’s) or Recurrent Neural Networks (RNN’s) can …

Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation
S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu – aclweb.org
… 2014. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pages 3104–3112. … 2015. Se- mantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745. …

Deep learning in the automotive industry: Applications and tools
A Luckow, M Cook, N Ashcraft, E Weill… – Big Data (Big Data), …, 2016 – ieeexplore.ieee.org
… Deep learning is extensively used by many online and mobile services, such as the voice recognition and dialog systems of Siri, the Google Assistant, Amazon’s Alexa … The landscape of infrastructure and tools for training and deploying deep neural networks is evolving rapidly. …

Dead Man Tweeting
D Nilsson, M Sahlgren, J Karlgren – Workshop on Collecting and …, 2016 – diva-portal.org
… Mikolov, T., Karafiát, M., Burget, L., Cernocký, J., and Khudanpur, S. (2010). Recurrent neural network based language model. … Semantically conditioned lstm- based natural language generation for spoken dialogue systems. In Proceedings of EMNLP, pages 1711–1721.

UNSUPERVISED USER INTENT MODELING BY FEATURE-ENRICHED MATRIX FACTORIZATION
YNCMS Alexander, IRA Gershman – cs.cmu.edu
… 17–20. [7] David Griol and Zoraida Callejas, “A neural network ap- proach to … [10] Yun-Nung Chen, William Yang Wang, and Alexander I Rud- nicky, “Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing,” in Proc. …

Improving Recognition Accuracy of Urdu Weather Service by Identifying Out-of-Vocabulary Words
S Irtza, A Anwar, S Hussain – Pakistan Journal of Engineering …, 2016 – journal.uet.edu.pk
… There is no robust speaker independent automatic speech recognition system in Urdu language that can be integrated with spoken dialogue system. Different domain specific Urdu ASR systems have been developed on limited vocabulary. Artificial neural networks [9] and HMM …

Towards Automatic Identification of Effective Clues for Team Word-Guessing Games
E Pincus, D Traum – pdfs.semanticscholar.org
… 10. Bibliographical References Burgener, R. (2006). Artificial neural network guess- ing method and game, October 12. US Patent App. 11/102,105. … who is this? quiz dialogue system and users’ evaluation. In 2008 IEEE Spoken Language Technology Workshop. 11. …

Automatic recognition of child speech for robotic applications in noisy environments
S Fernando, RK Moore, D Cameron, EC Collins… – arXiv preprint arXiv: …, 2016 – arxiv.org
… We used the latest deep neural network algorithms which provide a leap in performance over the traditional GMM approach, and apply data … We provide a close integration between the ASR module and the rest of the dialogue system, allowing the ASR to receive in real-time the …

Generating Clinically Relevant Texts: A Case Study on Life-changing Events
M Oak, AK Behera, TP Thomas, CO Alm… – pdfs.semanticscholar.org
… Andrej Karpathy. 2015. Char-RNN: Multi-layer re- current neural networks (LSTM, GRU, RNN) for character-level language models in torch. … Seman- tically conditioned lstm-based natural language gen- eration for spoken dialogue systems. arXiv preprint arXiv:1508.01745. …

An Ontology Model for Systems Engineering Derived from ISO/IEC/IEEE 15288: 2015: Systems and Software Engineering-System Life Cycle Processes
L Yang, K Cormican, M Yu – Computer, 2016 – waset.org”,
,Conferences.,

Multilingual Speech Processing through MFCCs feature extraction for multilingual speaker identification system
VK Jain, N Tripathi – i-manager’s Journal on Pattern …, 2016 – search.proquest.com
… Multilingual speaker identification and language identification are key to the development of spoken dialogue systems that can function in multilingual environments. Multilingual Speaker identification system refers to identifying persons from their voice. … rd. Neural Network. …

Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD)
E Bekele, J Wade, D Bian, J Fan… – Virtual Reality (VR), …, 2016 – ieeexplore.ieee.org
… underlying neural processes is poorly understood, some neural studies indicated that children with ASD employ different neural networks and rely … The VR-based facial emotional recognition in the presence of conversational dialog system presented a total of 12 conversational …

New intelligent control systems architectures based on dynamic intelligent systems
GV Rybina, VM Rybin – Intelligent Systems (IS), 2016 IEEE 8th …, 2016 – ieeexplore.ieee.org
… Hybridization methods of dynamic IES with neural networks (neurocon- trol), evolutionary methods and genetic modeling and other approaches used for the implementation of intelligent control brings a rather good result. IV. … Vol. 2. Intelligent dialogue systems. …

Scaling a Natural Language Generation System
J Pfeil, S Ray – pdfs.semanticscholar.org
… Shieber, 1988; Kay, 1996; White and Baldridge, 2003), systems that use forest architectures such as HALogen/Nitrogen, (Langkilde-Geary, 2002), systems that use tree conditional random fields (Lu et al., 2009), and newer systems that use recur- rent neural networks (Wen et al …

Report on the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval (ESAIR’15)
K Balog, J Dalton, A Doucet, Y Ibrahim – ACM SIGIR Forum, 2016 – dl.acm.org
… The second approach Matthew presented is to used a deep neural network architecture. In this architecture the network is passed in both closed < s,p,o > and open < s,r,o > examples at the same time. … Question answering and dialog systems • Health and medicine …

Effects of emotion on physiological signals
S Basu, A Bag, M Aftabuddin… – … ), 2016 IEEE Annual, 2016 – ieeexplore.ieee.org
… Depending upon nature of experiment, available data and expected outcome, various classifiers can be used, for example Regression Tree, Bayesian Networks, k Nearest Neighbour (kNN) [15] [32], Support Vector Machine (SVM) [15][23], Artificial Neural Network (ANN)[28 …

Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation
JZYCX Wang, PLW Xu – pdfs.semanticscholar.org
… Neural machine translation (NMT) aims at solving machine translation (MT) problems with purely neural networks and exhibits promising results in recent … Moreover, NMT models can also be easily adapted to other tasks such as dialog systems (Vinyals and Le, 2015), question …

Automatic creation of scenarios for evaluating spoken dialogue systems via user-simulation
R López-Cózar – Knowledge-Based Systems, 2016 – Elsevier
… Cover image Cover image. Automatic creation of scenarios for evaluating spoken dialogue systems via user-simulation. … This paper proposes a novel technique to create scenarios that can be used by a user simulator for exhaustively evaluating spoken dialogue systems. …

The Roberta IRONSIDE project: A dialog capable humanoid personal assistant in a wheelchair for dependent persons
H Sansen, MI Torres, G Chollet… – … for Signal and …, 2016 – ieeexplore.ieee.org
… G., Hennebert J., Petrovska- Delacrétaz D., Yvon F., Towards Fully Automatic Speech Processing Techniques for Interactive Voice Servers, Speech Processing, Recognition and Artificial Neural Networks, 1999, Proceedings … A framework for easier Spoken Dialog System Design …

Institute of Information Technology
J Lindner, W Teich, A Linduska, M Mostafa… – Journal of Siberian …, 2016 – uni-ulm.de
… 99-110, January 2016 Link to Document Bibtex. L. Pragst, S. Ultes and W. Minker Recurrent Neural Network Interaction Quality Estimation Proceedings of the 7th International Workshop On Spoken Dialogue Systems (IWSDS), Saariselka, Finland, January 2016 Bibtex. …

Automatic Diacritics Restoration for Dialectal Arabic Text
AA Zayyan, M Elmahdy, H binti Husni… – International Journal of …, 2016 – ijcis.info
… [18] G. Abandah, A. Graves and B. Al-Shag, “Automatic diacritization of Arabic text using recurrent neural networks,” International Journal … From 2007 to 2011, he was pursuing his Ph.D. degree at the Dialogue Systems Group, Institute of Information Technology at the University …

Statistical evaluation for quality of experience prediction based on quality of service parameters
S Aroussi, A Mellouk – Telecommunications (ICT), 2016 23rd …, 2016 – ieeexplore.ieee.org
… As a reminder, these seven methods are: Least Squares Regression (LSR), Artificial Neural Networks (ANN), Support Vector Machines (SVM … S. Möller, I. Wechsung, and C. Kühne, “Quality of Experiencing Multi-Modal Interaction,” in Spoken Dialogue Systems Technology and …

Translation (SedMT 2016)
D Xiong, K Duh, E Agirre, N Aranberri, H Wang – 2016 – anthology.aclweb.org
… Since then, he held post-doc positions at the University of Edinburgh, work- ing on spoken dialogue systems, and the La Sapienza University of Rome, conducting research … Kyunghyun’s main research interests include neural networks, generative models and their applications. …

Markov Chain Based QoS Support for Wireless Body Area Network Communication in Health Monitoring Services
RA Isabel, E Baburaj – Context, 2016 – waset.org”,
,Conferences.,

Prototypical Recurrent Unit
D Long, R Zhang, Y Mao – arXiv preprint arXiv:1611.06530, 2016 – arxiv.org
… Abstract The difficulty in analyzing LSTM-like recurrent neural networks lies in the complex structure of the recurrent unit, which induces highly complex nonlinear dynamics. In this paper, we design a new simple recurrent unit, which we call Prototypical Recurrent Unit (PRU). …

Speaker age estimation on conversational telephone speech using senone posterior based i-vectors
SO Sadjadi, S Ganapathy… – Acoustics, Speech and …, 2016 – ieeexplore.ieee.org
… Index Terms— Age estimation, deep neural networks, i-vector, linear discriminant analysis, support vector regression … of such speaker/user depen- dent content from speech has a wide range of applications includ- ing natural interaction with dialogue systems, caller-agent …

Spoken Language Understanding
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… overview of approaches to NLP and of its application in areas including mobile personal assistants, dialog systems, and question … Richard Socher’s Deep Learning for Natural Language Understanding (CS224d) at Stanford 10 ; Goldberg’s Primer on neural network models for …

Performance estimation of spontaneous speech recognition using non-reference acoustic features
L Guo, T Yamada, S Makino – Signal and Information …, 2016 – ieeexplore.ieee.org
… method to performance monitoring in speech recognition services and the use as a reliability measure in dialogue systems as noted … The acoustic models are gender independent triphone models with DNN-HMM (Deep Neural Network- Hidden Markov Model), which are trained …

Definition Modeling: Learning to define word embeddings in natural language
T Noraset, C Liang, L Birnbaum, D Downey – arXiv preprint arXiv: …, 2016 – arxiv.org
… We present several definition model architectures based on recur- rent neural networks, and experiment with the models over multiple data sets. … The definition models explored in this paper are based on a recurrent neural network language model (RNNLM) (Mikolov et al. …

KALDI GOES ANDROID
C Gaida, R Petrick, D Suendermann-Oeft – suendermann.com
… toolkits, even in the case of using Gaussian Mixture Models (GMM), instead of the even better performing Deep Neural Networks (DNN) for … K. Georgila, K. Sagae, R. Artstein, and D. Traum, “Practical evaluation of speech recogniz- ers for virtual human dialogue systems,” in Proc …

Mapping the Dialog Act Annotations of the LEGO Corpus into the Communicative Functions of ISO 24617-2
E Ribeiro, R Ribeiro, DM de Matos – arXiv preprint arXiv:1612.01404, 2016 – arxiv.org
… In this sense, automatic dialog act recognition is particularly important in the context of dialog systems [16]. … A set of 347 calls recorded during 2006 was later annotated by the Dialogue Systems Group at Ulm University, Germany. …

Automatic Detection of Hyperarticulated Speech
E Ribeiro, F Batista, I Trancoso, R Ribeiro… – Advances in Speech …, 2016 – Springer
… Go Bus Information System, which provides information about bus schedules in the city of Pittsburg, through spoken telephonic interaction with a dialog system. … We obtained a phone tokenization of the audio using the neural networks that are part of our in-house ASR system [9 …

Conflict Resolution in Robotics: An Overview
E Martinez-Martin, AP del Pobil – … Perspectives on Contemporary …, 2016 – books.google.com
… Cobano, Heredia, & Ollero, 2013), maze searching (Lumelsky & Harinarayan, 1997), potential fields (Ge & Cui, 1997; Barraquand & Latombe, 1991), neural networks (Kaidi, Lazaar, & … An Argumentation-based Dialogue System for Human- Robot Collaboration (Demonstration). …

Robust comprehension of natural language instructions by a domestic service robot
T Kobori, T Nakamura, M Nakano, T Nagai… – Advanced …, 2016 – Taylor & Francis”,
,,

Computational Interpersonal Communication: Communication Studies and Spoken Dialogue Systems
J David – communication+ 1, 2016 – scholarworks.umass.edu
… For more information, please contact scholarworks@library.umass.edu. Recommended Citation Gunkel, David J. Dr. (2016) “”Computational Interpersonal Communication: Communication Studies and Spoken Dialogue Systems,”” communication +1: Vol. …

Human-Robot Interaction Modelling for Recruitment and Retention of Employees
R Khosla, MT Chu, K Nguyen – … on HCI in Business, Government and …, 2016 – Springer
… The dialogue system is responsible for managing the job interview dialogue with the candidate. … Kohonen, T.: Learning vector quantization. In: Michael, AA (ed.): The Handbook of Brain Theory and Neural Networks, pp. 537–540. MIT Press, Cambridge (1998). 12. …

A Unified Knowledge Representation System for Robot Learning and Dialogue,
,N Shukla – 2016 – escholarship.org,
,Page 1. …,

Age driven automatic speech emotion recognition system
D Verma, D Mukhopadhyay – Computing, Communication and …, 2016 – ieeexplore.ieee.org
… For example the automatic dialogue systems at the call centers equipped with emotion recognition capability can recognize the customer’s mood … markov model (HMM) [12][17],Multilayer perceptron [13], Gaussian Mixture Model (GMM) [5][8][9], Artificial neural network (ANN) [8 …

AI for Online Criminal Complaints: From Natural Dialogues to Structured Scenarios
F Bex, J Peters, B Testerink – AI4J–Artificial Intelligence for Justice, 2016 – ecai2016.org
… rendered in an argumentation formalism [1], our main approach to dialogue management comes therefore from argumentation dialogue systems theory. … these features and a corpus of example data, we can train classifiers such as support vector machines, or neural networks. …

Thematic fit evaluation: an aspect of selectional preferences
A Sayeed, C Greenberg, V Demberg – ACL 2016, 2016 – anthology.aclweb.org
… This is particularly important as dialog systems grow steadily less task-specific. … 2014. A neural network ap- proach to selectional preference acquisition. In Proceedings of the 2014 Conference on Empir- ical Methods in Natural Language Processing (EMNLP). pages 26–35. …

Gaussian Attention Model and Its Application to Knowledgebase Embedding and Question Answering
L Zhang, J Winn, R Tomioka – arXiv preprint arXiv:1611.02266, 2016 – arxiv.org
… With the proposed attention model, a neural network has the additional degree of freedom to control the focus of its attention from a laser sharp attention to a broad attention. … 1 INTRODUCTION There is a growing interest in incorporating external memory into neural networks. …

Which argument is more convincing? Analyzing and predicting convincingness of Web arguments using bidirectional LSTM
I Habernal, I Gurevych – … of the 54th Annual Meeting of …, 2016 – informatik.tu-darmstadt.de
… We em- ploy SVM model with rich linguistic features as well as bidirectional Long Short-Term Memory (BLSTM) neural networks because of their excel- lent performance across various end-to-end NLP tasks (Goodfellow et al., 2016; Piech et al., 2015; Wen et al., 2016; Dyer et al …

A Corpus for Event Localization,
,C Ward – 2016 – bir.brandeis.edu,
,”… With recent advances in GPU-based learning and large-scale data sets, neural networks have been able to learn representations directly from images that result in very low error rates on data sets with hundreds of scene categories. … 19 4.1.2 Neural Networks . . . . . …

Overview of ntcir-12
K Kishida, M Kato – Proceedings of the NTCIR Conference on …, 2016 – research.nii.ac.jp
… we apply a rescoring method to improve the STD accuracy that contains highly ranked candidates [3]. Lastly, a rescoring method is applied to compare a query with spoken documents in more detail by using the posterior probability obtained from Deep Neural Network (DNN) [4 …

Combination of Empirical Mode Decomposition Components of HRV Signals for Discriminating Emotional States
A Goshvarpour, A Abbasi… – Iranian Journal of Medical …, 2016 – ijmp.mums.ac.ir
… Afterwards, a probabilistic neural network was used to classify the features into emotional classes. … i i m rB m N lm N rB 1 )( )( (10) 2.2.3. Classification In previous research, probabilistic neural network (PNN) has shown proper performance in classification problems [52]. …

Combining the evidences of temporal and spectral enhancement techniques for improving the performance of Indian language identification system in the presence of …
PK Polasi, KSR Krishna – International Journal of Speech Technology, 2016 – Springer
… Although methods like Gaussian mixture models, hidden Markov models and neural networks are used for identifying lan- guages the problem of language identification in noisy environments could not be addressed so far. …

Performance of Multimodal Biometric System Based on Level and Method of Fusion
M Pathak, N Srinivasu – Advances in Computing Applications, 2016 – Springer
… Domains such as sports video analysis and multimodal dialog system mostly use rule-based fusion method. … Methods for classifications are support vector machine (SVM), Bayesian interface, dynamic Bayesian network (DBN), neural network (NN), Dempster–Shafer theory and …

Detecting affective states from text based on a multi-component emotion model
Y Gao, W Zhu – Computer Speech & Language, 2016 – Elsevier
… Recently, the Deep Neural Network (DNN) has gained great successes in the applications of speech recognition (Deng et al., 2012) and image recognition (Hinton and Salakhutdinov, 2006). … DSN is a variant of the multi-layered Deep Neural Network (DNN) (see Fig. …

Automatic Spoken Customer Query Identification for Arabic Language
AM Qaroush, A Hanani, B Jaber, M Karmi… – Proceedings of the …, 2016 – dl.acm.org
… 4. CONCLUSION In this paper, an automated task-oriented Arabic dialogue system which is capable to determine the topic of spoken question asked … ASR can be improved by both using more adaptation data and using state of the art techniques such as deep neural networks. …

Two-stage multi-intent detection for spoken language understanding
B Kim, S Ryu, GG Lee – Multimedia Tools and Applications, 2016 – Springer
… in Proc. ICML 5. Lee C, Jung S, Kim K, Lee D, Lee GG (2010) Recent approaches to dialog management for spoken dialog systems. … in Proc. ASRU 9. Mikolov T, Karafi’at M, Burget L, Cernock’y J, Khudanpur S (2010) Recurrent neural network based language model. …

Automatically Classifying Self-Rated Personality Scores from Speech
G An, SI Levitan, R Levitan, A Rosenberg… – Interspeech …, 2016 – venus.cs.qc.edu
… cally identifying the NEO-FFI Big Five personality traits from speech, which will be useful for applications such as dialogue systems. … J. Cui, B. Ramabhadran, A. Rosenberg, MS Rasooli, O. Rambow, N. Habash, and V. Goel, “Improving deep neural network acoustic modeling for …

“Confused, bored, excited? An emotion based approach to the design of online learning systems”
T Kung-Keat, J Ng – 7th International Conference on University Learning …, 2016 – Springer
… Neural Networks, 18(4), 389–405.CrossRefGoogle Scholar. Hancock, JT, Landrigan, C., & Silver, C. (2007, April 28–May 3). Expressing emotion in text-based … Paper presented at the IEEE tutorial and research workshop on Perception in multimodal dialogue systems (pp. …

Detecting Sarcasm in Multimodal Social Platforms
R Schifanella, P de Juan, J Tetreault… – Proceedings of the 2016 …, 2016 – dl.acm.org
… The second method adapts a vi- sual neural network initialized with parameters trained on ImageNet to multimodal sarcastic posts. … The second method adapts a visual neural network initialized with parameters trained on ImageNet to multimodal (text+image) sarcastic posts. …

Prototype-based class-specific nonlinear subspace learning for large-scale face verification
A Iosifidis, M Gabbouj – … Tools and Applications (IPTA), 2016 6th …, 2016 – ieeexplore.ieee.org
Page 1. Prototype-based Class-Specific Nonlinear Subspace Learning for Large-Scale Face Verification Alexandros Iosifidis and Moncef Gabbouj Department of Signal Processing, Tampere University of Technology, Finland …

The Dark Side of NLP: Gefahren automatischer Sprachverarbeitung,
,M Strube – 2016 – pdfs.semanticscholar.org,
,”… Johnston, Michael, Patrick Ehlen, Frederick G. Conrad, Michael F. Schober, Christopher An- toun, Stefanie Fail, Andrew Hupp, Lucas Vickers, Huiying Yan & Chan Zhang (2013). Spo- ken dialog systems for automated survey interviewing. …

Cybersecurity methodology for a multi-tier mission and its application to multiple mission paradigms
J Straub – Aerospace Conference, 2016 IEEE, 2016 – ieeexplore.ieee.org
… Generic prioritization framework for target selection and instrument usage for reconnaissance mission autonomy. Presented at Neural Networks, 2006. IJCNN’06. … Presented at Proc. Workshop on Dialogue Systems: Interaction, Adaptation and Styles of Management. 10th Conf. …

N-gram Approximation of Latent Words Language Models for Domain Robust Automatic Speech Recognition
R Masumura, T Asami, OBA Takanobu… – … on Information and …, 2016 – jstage.jst.go.jp
… clustered into some groups. Also, neural network LMs and recurrent neural net- work LMs (RNNLMs) can reduce dimensionality on the ba- sis of learning the distributed representation of words [9]– [11]. To further advance towards …

Semi-supervised acoustic model training by discriminative data selection from multiple ASR systems’ hypotheses
S Li, Y Akita, T Kawahara – IEEE/ACM Transactions on Audio, Speech …, 2016 – dl.acm.org
… transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In …

ActBot: Sharing high-level robot AI scripts
C Creusot – Robot and Human Interactive Communication (RO- …, 2016 – ieeexplore.ieee.org
… This means that the number, nature and 1This does not prevent the user from using emerging next-best-action recommendation engines such as POMDP or Neural Network. … A neural network or POMDP system are based on human generated training data and will always retur

Few-Shot Object Recognition from Machine-Labeled Web Images
Z Xu, L Zhu, Y Yang – arXiv preprint arXiv:1612.06152, 2016 – arxiv.org
… Abstract With the tremendous advances of Convolutional Neural Networks (ConvNets) on object recognition, we can now obtain reliable enough machine-labeled annotations easily by predictions from off-the-shelf ConvNets. … External Memory in Neural Networks. …

Global Brain That Makes You Think Twice
R Rzepka, M Mazur, A Clapp… – 2016 AAAI Spring …, 2016 – researchgate.net
… Text min- ing for wellbeing: Selecting stories using semantic and prag- matic features. In Artificial Neural Networks and Machine Learning–ICANN 2012. … Kimura, Y.; Rzepka, R.; and Takamaru, K. 2015. Proposal of Radiobots based spoken dialogue system (in Japanese). …

From Alan Turing to modern AI: practical solutions and an implicit epistemic stance
GF Luger, C Chakrabarti – AI & SOCIETY, 2016 – Springer
… From the empiricist perspective, neural networks and ”deep” semantic networks were also designed to capture associations in collected sets of data and then, once trained, to … 5, monitor whether the human agent’s implied goal is met by the computational dialogue system. …

An Information Reinstatement Dealing with Machine Learning
F Parwej, H Alquhayz – Transactions on Machine Learning …, 2016 – scholarpublishing.org
… For that purpose, we propose different models based on kernel machines or neural networks adapted to different retrieval contexts. … Large-scale retrieval systems, such as the Lockheed Dialog system, came into use early in the 1970s. …

Global Brain That Makes You Think Twice
R Rafal, M Michal, C Austin… – AAAI Spring Symposium …, 2016 – eprints.lib.hokudai.ac.jp
… Text min- ing for wellbeing: Selecting stories using semantic and prag- matic features. In Artificial Neural Networks and Machine Learning–ICANN 2012. … Kimura, Y.; Rzepka, R.; and Takamaru, K. 2015. Proposal of Radiobots based spoken dialogue system (in Japanese). …

Computer-assisted pronunciation training: From pronunciation scoring towards spoken language learning
NF Chen, H Li – … Summit and Conference (APSIPA), 2016 Asia- …, 2016 – ieeexplore.ieee.org
… and Jinsong Zhang, “A study on robust detection of pronunciation erroneous tendency based on deep neural network,” in 16th … and Mattias Heldner, “An instantaneous vector representation of delta pitch for speaker-change prediction in conversation dialogue system,” in ICASSP …

Adaptive robot assisted therapy using interactive reinforcement learning
K Tsiakas, M Dagioglou, V Karkaletsis… – … Conference on Social …, 2016 – Springer
… In: International Joint Conference on Neural Networks, pp. … 133–156. Springer, Heidelberg (2015). 19. Rieser, V., Lemon, O.: Reinforcement Learning for Adaptive Dialogue Systems: A Data-driven Methodology for Dialogue Management and Natural Language Generation. …

Symbol emergence in robotics: a survey
T Taniguchi, T Nagai, T Nakamura, N Iwahashi… – Advanced …, 2016 – Taylor & Francis”,
,,

“The Influence of Annotation, Corpus Design, and Evaluation on the Outcome of Automatic Classification of Human Emotions”
M Kächele, M Schels… – Front. ICT 3: 27. doi: …, 2016 – journal-cdn.frontiersin.org
… Subjects areasked topacka suitcase for a voyage to an unknown place using a voice controlled dialog system. … Additional events are stimulated by setting malfunctions of the dialog system using external manipu- lations of the experimenter. …

Development of a smart algorithm for content analysis of non-verbal information in speech,
,H Abdelwahab – irit.fr,
,”… 20 4.2.2 Neural Networks . . … One of the goals is the Automated Speech Recognition system for under- standing content of speech, analyzing and processing this data for more genius Dialogue Systems like ”Siri, Google talk…”, However, sometimes, the content of speech ”Verbal …

“Text analytics in industry: Challenges, desiderata and trends”
A Ittoo, LM Nguyen, A van den Bosch – Computers in Industry, 2016 – Elsevier
The recent decades have witnessed an unprecedented expansion in the volume of unstructured data in digital textual formats. Companies are now starting to recogn.

From VoiceXML to multimodal mobile Apps: development of practical conversational interfaces
D Griol, JM Molina – 2016 – gredos.usal.es
… Our technique employs a statistical model based on neural networks that takes into account the history of the dialog up to … areas of speech recognition, natural language processing and speech synthesis, the first research initiatives related to spoken dialog systems appeared in …

Understanding Satirical Articles Using Common-Sense
D Goldwasser, X Zhang – Transactions of the Association for …, 2016 – transacl.org
… To demonstrate the robust- ness of our COMSENSE approach we use the first dataset for training, and the second as out-of-domain test data. We compare COMSENSE to several com- peting systems including a state-of-the-art Convo- lutional Neural Network (Kim, 2014). …

On Generating Characteristic-rich Question Sets for QA Evaluation
Y Su, H Sun, B Sadler, M Srivatsa – 2016 – aclweb.org
… (2016) use a recurrent neural network to auto- matically formulate questions. … pipeline to generate QA datasets. For example, Serban et al. (2016) automatically convert Free- base triples into questions with a neural network. …

A taxonomy for user models in adaptive systems: special considerations for learning environments
N Medina-Medina… – The Knowledge …, 2016 – Cambridge Univ Press
… Among these, the taxonomy of beliefs and goals for UMs in dialog systems proposed in Kobsa (1989) should be noted. … 2001) such as: linear models, term frequency, inverse document frequency (TFIDF)-based models, Markov models, neural networks, classification, rule …

Layered hidden Markov models to recognize activity with built-in sensors on Android smartphone
YS Lee, SB Cho – Pattern Analysis and Applications, 2016 – Springer
… data. Gy?rbíró et al. developed a system to recognize a person’s activities from acceleration data using a feed-forward neural network [18]. Song et al. proposed … al. used hierarchical HMM for spoken dialogue system [27]. Wang …

Review of state-of-the-arts in artificial intelligence. Present and future of AI.,
,V Shakirov – alpha.sinp.msu.ru,
,”… [18] Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau. ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 [19 …

Expanding science and technology thesauri from bibliographic datasets using word embedding
T Kawamura, K Kozaki, T Kushida… – Tools with Artificial …, 2016 – ieeexplore.ieee.org
… [3] K. Hashimoto, M. Miwa, Y. Tsuruoka and Takashi Chikayama: “Simple Customization of Recursive Neural Networks for Semantic … and R. Sarikaya: “En- riching Word Embeddings Using Knowledge Graph for Semantic Tagging in Conversational Dialog Systems,” In Proc. …

Using Past Speaker Behavior to Better Predict Turn Transitions
T Meshorer, PA Heeman – Interspeech 2016, 2016 – csee.ogi.edu
… L. Deng, D. Yu, GE Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, TN Sainath et al., “Deep neural networks for acoustic … [19] M. Atterer, T. Baumann, and D. Schlangen, “Towards incremen- tal end-of-utterance detection in dialogue systems.” in COLING …

Towards an intelligent system for generating an adapted verbal and nonverbal combined behavior in human–robot interaction
A Aly, A Tapus – Autonomous Robots, 2016 – Springer
… Another interesting theoretical study was discussed in Wahlster and Kobsa (1989) and Zukerman and Litman (2001), where they focused on the field of user modeling (ie, understanding the user’s beliefs, goals, and plans) in artificial intelligence dialog systems, and illustrated …

Multimodal and multi-party social interactions
Z Yumak, N Magnenat-Thalmann – Context Aware Human-Robot and …, 2016 – Springer
… Support vector machines (SVMs), Dempster–Shafer theory, dynamic Bayesian networks (DBNs), neural networks (NNs) and maximum entropy model belong to this category. … Bohus et al. [6] developed a dynamic engagement model where the dialogue system learned to predict …

Comparison of text-independent original speaker recognition from emotionally converted speech
J P?ibil, A P?ibilová – Recent Advances in Nonlinear Speech Processing, 2016 – Springer
… 1. Skowron, M., Rank, S., Swiderska, A., Küster, D., Kappas, A.: Applying a text-based affective dialogue system in psychological research: case studies on the … Tóth, L., Grósz, T.: A Comparison of deep neural network training methods for large vocabulary speech recognition. …

Automatic Generation of Proper Noun Entries in a Speech Recognizer for Local Information Recognition
K Shiga, T Nose, A Ito, R Masumura, H Masataki – 2016 – researchgate.net
… We used a deep-neural-network (DNN) based acoustic model, having five intermediate layers. … Komatani, T. Ogata and HG Okuno, Expanding Vocabulary for Recognizing User’s Abbreviations of Proper Nouns without Increasing ASR Error Rates in Spoken Dialog Systems, Proc …

FEDERICO II,
,S Balbi – researchgate.net,
,”… Other methods include decision trees, rule induction, neural networks, clustering methods, association rules, feature … disambiguation; Parsing; Machine Translation; Information Translation; 3. Hard: Text Summarization; Machine dialog system (also defined “”Intelli- …

Deep learning for sentiment analysis
LM Rojas?Barahona – Language and Linguistics Compass, 2016 – Wiley Online Library
… 3.1 Feed-forward neural network. Artificial neural networks are composed of small processing units, namely, neurons, which are connected to each other by weighted connections. … 3.2 Conventional neural networks. In this section, we present sequence NNs. …

Recognition System for Home-Service-Related Sign Language Using Entropy-Based $ K $-Means Algorithm and ABC-Based HMM
THS Li, MC Kao, PH Kuo – IEEE Transactions on Systems, Man, …, 2016 – ieeexplore.ieee.org
… Hernandez-Rebollar et al. [19] applied hidden Markov models (HMMs) and neural network to trans- late isolated gestures of ASL into spoken and written words. … Vamplew [34] devel- oped a sign language recognition system using neural network. …

A novel approach to improve the planning of adaptive and interactive sessions for the treatment of Major Depression
A Bresó, J Martínez-Miranda, E Fuster-García… – International Journal of …, 2016 – Elsevier
… Another related work was presented by Kharat and Dudul (2009) in which they presented a system able to adapt the conversation content with humans based on emotion recognition from facial expressions using neural networks. …

Seqgan: sequence generative adversarial nets with policy gradient
L Yu, W Zhang, J Wang, Y Yu – arXiv preprint arXiv:1609.05473, 2016 – arxiv.org
… Re- cently, recurrent neural networks (RNNs) with long short- term memory (LSTM) cells (Hochreiter and Schmidhuber 1997) have shown excellent performance ranging from nat- ural language generation to handwriting generation (Wen et al. 2015; Graves 2013). …

An Interactive Learning and Adaptation Framework for Adaptive Robot Assisted Therapy
K Tsiakas, M Papakostas, B Chebaa, D Ebert… – Proceedings of the 9th …, 2016 – dl.acm.org
… IEEE, 2013. [8] S. Janarthanam and O. Lemon. Adaptive generation in dialogue systems using dynamic user modeling. Computational Linguistics, 2014. … ACM, 2015. [25] P. Wang et al. Deep convolutional neural networks for action recognition using depth map sequences. …

An Exploratory Study on Process Representations,
,CN Naik – 2016 – search.proquest.com,
,”… This chapter provides the background material and literature review for semantic role labeling and recurrent neural networks. … in NLP and have been shown to benet question answering [7, 8], textual entailment [9], machine translation [1012], and dialogue systems [13, 14 …

An integrated system for interactive continuous learning of categorical knowledge
D Sko?aj, A Vre?ko, M Mahni?, M Janí?ek… – … of Experimental & …, 2016 – Taylor & Francis”,
,,

Challenges in sentiment analysis
SM Mohammad – A Practical Guide to Sentiment Analysis, D. …, 2016 – saifmohammad.com
… also propose certain embeddings-based recursive neural network models to capture the impact of negators more precisely. … Lucas, Agrawal, Park, et al., 2013), tracking the stock market (Bollen, Mao, & Zeng, 2011), and improving automatic dialogue systems (Velásquez, 1997 …

Real-Life Robustness
F Eyben – Real-time Speech and Music Classification by Large …, 2016 – Springer
… Yet, all of these systems do not use adaptive context learning as provided by Long Short-Term Memory Recurrent Neural Networks (LSTM-RNNs) (Sect. … 2012) are used to overcome those limitations. Standard Recurrent Neural Networks (RNNs) (as applied by Gemello et al. …

Knowledge Management & E-Learning
M Samarakou, ED Fylladitakis, D Karolidis, WG Früh… – pureapps2.hw.ac.uk
… Stathacopoulou, R., Grigoriadou, M., Samarakou, M., & Mitropoulos, D. (2007). Monitoring students actions and using teachers expertise in implementing and evaluating the neural network-based fuzzy diagnostic model. … W-ReTuDiS: A reflective tutorial dialogue system. …

Towards addressee recognition in smart robotic environments: an evidence based approach
V Richter, F Kummert – Proceedings of the 1st Workshop on Embodied …, 2016 – dl.acm.org
… Are you talking to me ? Improving the robustness of dialogue systems in a multi party HRI scenario by incorporating gaze direction and lip movement of attendees. … IEEE Transactions on Neural Networks, 13(4), jul 2002. [28] T. Yamazaki. Beyond the Smart Home. …

Emotion Modelling via Speech Content and Prosody: In Computer Games and Elsewhere
B Schuller – Emotion in Games, 2016 – Springer
… approaches include a range of variants of Non-Negative Matrix Factorisation (NMF) [59] or the usage of (recurrent) neural networks (preferably with … synthesiser is often given in computer games, this may be a promising road in affect recognition for gaming or dialogue systems. …

Review of state-of-the-arts in artificial intelligence with application to AI safety problem
V Shakirov – arXiv preprint arXiv:1605.04232, 2016 – arxiv.org
… [23] Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau. ”Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models” http:// arxiv.org/abs/1507.04808 [24 …

Semi-Supervised Learning with Sparse Autoencoders in Automatic Speech Recognition,
,AK Dhaka – 2016 – diva-portal.org,
,”… 15 3.1.2 Recurrent Neural Networks . . . . . … In dialogue systems, this tex- tual representation is fed to a language understanding module that extracts the semantic information to be handled by a dialogue manager. …

Investigating Multimodal Audiovisual Event Detection and Localization
N Vryzas, R Kotsakis, CA Dimoulas… – Proceedings of the Audio …, 2016 – dl.acm.org
… Specifically, three supervised machine learning techniques were utilized in the WEKA environment [30], namely the Decision Tree method (J48), the statistical Logarithmic Regression (Log.Reg.) and the Artificial Neural Networks (ANN) topologies [11]-[13], [30]. …

Design and evaluation of reduced-size feature sets for the assessment of sincerity in speech
EM Albornoz, CE Martinez – Computational Intelligence and …, 2016 – ieeexplore.ieee.org
… languages, for example English and French [5,6]. These features have been used to build robust spoken dialogue systems, which are … of these new sets of features with other clas- sification/regression schemes, for example, dynamic models or deep neural networks that exploit …

Solving Verbal Questions in IQ Test by Knowledge-Powered Word Embedding
H Wang, F Tian, B Gao, C Zhu, J Bian, TY Liu – aclweb.org
… studies or distributional word representa- tions based on co-occurrence matrix between words such as LSA (Dumais et al., 1988) and LDA (Blei et al., 2003), distributed word representations are usually low-dimensional dense vectors trained with neural networks by maximizing …

Some essential skills and their combination in an architecture for a cognitive and interactive robot
S Devin, G Milliez, M Fiore, A Clodic… – arXiv preprint arXiv: …, 2016 – arxiv.org
… In [14] we have used this framework along with a dialog system to implement a situated dialog and consequently to improve dialog in term of efficiency and … [11] C. Vesper, S. Butterfill, G. Knoblich, and N. Sebanz, “A minimal architecture for joint action,” Neural Networks, vol. …

A New Approach of Facial Expression Recognition for Ambient Assisted Living
Y Yaddaden, A Bouzouane, M Adda… – Proceedings of the 9th …, 2016 – dl.acm.org
… For the classification step, for each kind of feature, they used a different classification method. For the first type of feature (ICA coefficients), ANN (Artifi- cial Neural Network) classifier was used and for the second type of feature (Distance ratios), KNN classifier has been used. …

A Novel Lip Descriptor for Audio-Visual Keyword Spotting Based on Adaptive Decision Fusion
P Wu, H Liu, X Li, T Fan, X Zhang – IEEE Transactions on …, 2016 – ieeexplore.ieee.org
… 3) Train the neural network using the input reliabilities and the corresponding optimal weights. B. Two-Step Keyword Spotting Strategy … This method is primarily used in application fields such as dialogue systems, and command control and informa- tion consultation. …

Interaction Style Recognition Based on Multi-layer Multi-view Profile Representation
WL Wei, JC Lin, CH Wu – IEEE Transactions on Affective …, 2016 – ieeexplore.ieee.org
… Recently, automatic IS recognition is becoming increasingly important in the design of a dialogue system for harmonious interaction. … Similar to the multilayer architectures in deep neural networks, a multi-layer multi-view IS profile representation method, structured layer by layer …

Deadly Banquet
J SUNDLING, G AXELSSON, H NILSSON… – publications.lib.chalmers.se
… 14 3.3 The dialogue system ….. 16 … Neural network was also considered too complicated and somewhat unsuited to this type of problem. Neural networks are non-deterministic and also require a concrete goal to strive for. …

Children’s Speech Recognition Under Mismatched Condition: A Review
Y Sunil, SRM Prasanna, R Sinha – IETE Journal of Education, 2016 – Taylor & Francis
… 1, 1993, pp. 5–16.
S. Schötz, “A perceptual study of speaker age,” in Working Paper 49. Dept. Linguistic, Lund University, 2001, pp. 136–9.
A. Potamianos and S. Narayanan, “Spoken dialog systems for children,” in Proc. IEEE ICASSP, Seattle, WA, Vol. 1, 1998, pp. …

Psychophysiology in games
GN Yannakakis, HP Martinez, M Garbarino – Emotion in Games, 2016 – Springer
… Available preference learning approaches include linear discriminant analysis, decision trees, artificial neural networks (shallow and deep architectures … T, Potamianos A (2009) Towards adapting fantasy, curiosity and challenge in multimodal dialogue systems for preschoolers. …

Training agents with interactive reinforcement learning and contextual affordances
F Cruz, S Magg, C Weber… – IEEE Transactions on …, 2016 – ieeexplore.ieee.org
… This contextual affordance model allows us to determine beforehand when it is possible to apply an affordance using an artificial neural network (ANN) to learn the relationship with the state, the action, and the object as inputs and the effect as output. …

Adaptive Visualization
K Nazemi – Adaptive Semantics Visualization, 2016 – Springer
… L. Fausett, Fundamentals of Neural Networks (Prentice-Hall, Englewood Cliffs, 1994)MATH. 40. … 948–960. 80. W. Wahlster, A. Kobsa, in User Models in Dialog Systems, Symbolic Computation, ed. by A. Kobsa, W. Wahlster (Springer, Berlin, 1989), pp. 4–34. …

Challenges on Multimedia for Decision-Making in the Era of Cognitive Computing
MF Moreno, R Brandão… – Multimedia (ISM), 2016 …, 2016 – ieeexplore.ieee.org
… dialog systems capable of interacting naturally with people. Visual machine perception through CV techniques often focuses on automatically labeling image and video content. With the increased availability of large-scale computing and advances in neural network algorithms …

Emotional speech feature normalization and recognition based on speaker-sensitive feature clustering
C Huang, B Song, L Zhao – International Journal of Speech Technology, 2016 – Springer
… In their study, one-class-in-one neural networks are used, and a recognition rate of approximately 50 % was achieved on eight emotions. … Towards real life applications in emotion recognition, In Proceedings of the workshop on affective dialogue systems, Kloster Irsee (pp. …

Associating gesture expressivity with affective representations
L Malatesta, S Asteriadis, G Caridakis… – … Applications of Artificial …, 2016 – Elsevier
Affective computing researchers adopt a variety of methods in analysing or synthesizing aspects of human behaviour. The choice of method depends on which behavi.

The Comprehension of Figurative Language: What Is the Influence of Irony and Sarcasm on NLP Techniques?
L Weitzel, RC Prati, RF Aguiar – Sentiment Analysis and Ontology …, 2016 – Springer
… sighted users; automatic report generation (possibly multilingual); machine translation; plagiarism detection tools; email understanding and dialogue systems [5]. … Recent work [39, 44] uses neural networks for learning distributed representations based on word co-occurrence. …

Incorporating android conversational agents in m?learning apps
D Griol, JM Molina, Z Callejas – Expert Systems, 2016 – Wiley Online Library
… To do this, a statistical dialogue model based on neural networks is generated taking into account the contextual information and the history of the dialogue up to the current moment. The next response of the conversational agent is selected by means of this model. …

“Enabling collaborative geoinformation access and decision?making through a natural, multimodal interface”
R Sharma, I Rauschert, H Wang – Citeseer
… 8 (query of spatial objects in a limited geographic region). This is arguably one of the most successful efforts thus far in verbal dialogue systems to databases (whether spatial or not), however, Glass and colleagues (Glass et al., 1995) reported performance of only …

Research Progress of Artificial Psychology and Artificial Emotion in China
Z Wang, L Xie, T Lu – CAAI Transactions on Intelligence Technology, 2016 – Elsevier
… Some famous conferences include the Affective Interactions Symposium in the 2005 International Conference on Intelligent User Interfaces; the biannual FGR of the IEEE Face Information Processing International Conference; Affective Dialogue Systems symposium in 2004 …

A statistical sample-based approach to GMM-based voice conversion using tied-covariance acoustic models
S Takamichi, T Tomoki, G Neubig, S Sakti… – … on Information and …, 2016 – search.ieice.org
Page 1. 2490 IEICE TRANS. INF. & SYST., VOL.E99–D, NO.10 OCTOBER 2016 PAPER Special Section on Recent Advances in Machine Learning for Spoken Language Processing A Statistical Sample-Based Approach to GMM-Based Voice …

Affective Conversational Interfaces
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… (2003) achieved an emotion classification accuracy of 83 %; and Haag et al. (2004) classified arousal and valence separately using a neural network classifier and obtained recognition accuracy rates of 96.6 and 89.9 %, respectively. Kim et al. …

Specification of an Architecture for Self-organizing Scheduling Systems
A Madureira, I Pereira, B Cunha – International Conference on Intelligent …, 2016 – Springer
… Springer, New York (2013)MATHGoogle Scholar. 10. Ferguson, G., Allen, J.: Mixed-initiative dialogue systems for collaborative problem-solving. AI Mag. … Cunha, B., Madureira, A., Pereira, JP: User modelling in scheduling system with artificial neural networks. …

ALE for robots! A single-channel approach to robot self-noise cancellation
J Taghia, D Kolossa, R Martin – Acoustic Signal Enhancement ( …, 2016 – ieeexplore.ieee.org
… Unlike methods based on NMF, dictionary-learning, or deep neural networks it does not require training on specific types of noise. … Sawada, H. Saruwatari, K. Shikano, and T. Takatani, “Semi-blind suppression of internal noise for hands-free robot spoken dialog system,” in 2009 …

Algorithms for Batch Hierarchical Reinforcement Learning
T Zhao, M Gowayyed – arXiv preprint arXiv:1603.08869, 2016 – arxiv.org
… in a prac- tical problem that is expensive in collecting new samples, such as education, spoken dialog system and medical … using function approximation is that it can incorporate powerful supervised regression methods, such as Gaussian Processes or Neural Networks to scale …

Ensemble softmax regression model for speech emotion recognition
Y Sun, G Wen – Multimedia Tools and Applications, 2016 – Springer
… With the growth in the electronic and computer technologies, new spoken dialogue systems with emotion recognition capability are needed. … Yongming Huang [19] presented an emotion recognition system using the stacked gener- alization ensemble neural network for special …

Real-Time Coordination in Human-Robot Interaction Using Face and Voice
G Skantze – AI Magazine, 2016 – speech.kth.se
… Traditionally, spoken dialogue systems have rested on a very simplistic model of turn-taking, where a certain amount of silence (say 700-1000ms) is used as an indicator for transition- relevance places. … Obliged using an artificial neural network with dif- ferent sets of features. …

Predicting sentential semantic compatibility for aggregation in text-to-text generation
V Chenal, JCK Cheung – pdfs.semanticscholar.org
… Other clustering models, using neural networks to learn a similarity measure for instance, can also be considered. … Amanda Stent, Rashmi Prasad, and Marilyn Walker. 2004. Trainable sentence planning for complex information presentation in spoken dialog systems. …

Adaptive dissemination for mobile electronic health record applications with proactive situational awareness
D Preuveneers, NZ Naqvi… – … (HICSS), 2016 49th …, 2016 – ieeexplore.ieee.org
Page 1. Adaptive Dissemination for Mobile Electronic Health Record Applications with Proactive Situational Awareness Davy Preuveneers, Nayyab Zia Naqvi, Arun Ramakrishnan, Yolande Berbers and Wouter Joosen iMinds …

Multi-party language interaction in a fast-paced game using multi-keyword spotting
JF Lehman, N Wolfe, A Pereira – International Conference on Intelligent …, 2016 – Springer
… Automatic speech recognition Child-computer interaction Multi-party interaction Spoken dialog systems. 1 Introduction. … We tested several algorithms – decision trees, neural networks and Support Vector Machines (SVMs) – and several values for the number of prior segments. …

Multi-behavioral Sequential Prediction for Collaborative Filtering
Q Liu, S Wu, L Wang – arXiv preprint arXiv:1608.07102, 2016 – pdfs.semanticscholar.org
… As two classical neural network methods for modeling sequences, recurrent neural networks can not well model short-term contexts, and the log-bilinear model is not suitable for long-term contexts. … Fig. 2. Illustration of the Recurrent Neural Networks (RNN) model. …

INTELLIGENT STUDENTS’PERFORMANCE ENHANCER (ISPE)
AR Khan, QM Khalil, MA Farooq – researchgate.net
… Economides, Anastasios A. Prediction of Student’s Mood during an Online Test Using Formula-based and Neural Network-based Method. … Laila Dybkjaer and Niels Ole Bernsen (eds.), Natural, Intelligent and Effective Interaction in Multimodal Dialogue Systems, © 2003 Kluwer …

Decision Support System in Marine Navigation
Z Pietrzykowski, P Wo?ejsza – … on Transport Systems Telematics, TST 2016, …, 2016 – Springer
… use methods and tools of knowledge engineering, including methods and tools of artificial intelligence: artificial neural networks, fuzzy logic … Besides, such system should be a dialog system (question – answer – solution – decision), making it possible for the officer to update …

Semeval-2016 task 2: Interpretable semantic textual similarity
E Agirre, A Gonzalez-Agirre, I Lopez-Gazpio… – Proceedings of …, 2016 – aclweb.org
… The bottom layer consists of a re- current neural network that processes input and feeds composed semantic feature vectors to the … the Answer- Students corpus, which consists of the interactions between students of electronics and the BEETLE II tutorial dialogue system. …

MULTI-OBJECTIVE GENETIC ALGORITHMS AS AN EFFECTIVE TOOL FOR FEATURE SELECTION IN THE SPEECH-BASED EMOTION RECOGNITION …
CY Brester, OE Semenkina, MY Sidorov – christinabrester.com
… One of the obvious ways to improve the intellectual abilities of spoken dialogue systems is related to their personalization. … 2) Multilayer Perceptron (MLP). A feedforward neural network with one hidden layer is trained with the error backpropagation algorithm (BP). …

Usability Testing with Children: BatiKids Case Study
H Rante, L De Araújo, H Schelhowe – Context, 2016 – waset.org
… Heart-Rate Resistance Electrocardiogram Identification Based on Slope-Oriented Neural Networks. … This research provides a heartbeat segment method with slope-oriented neural networks against the ECG morphology changes due to high intensity HRs. …

Speech Recognition Challenges in the Car Navigation Industry
A Vékony – International Conference on Speech and Computer, 2016 – Springer
… We will mention typical context designs, dialogue systems and address search, and we will show how the common … Upcoming connected digital assistants feature semantic analysis, advanced machine learning based on neural networks and artificial intelligence techniques. …

Prediction of Deception and Sincerity from Speech using Automatic Phone Recognition-based Features
R Herms – Interspeech 2016, 2016 – researchgate.net
… [11] J. Tepperman, DR Traum, and S. Narayanan, “” yeah right”: sarcasm recognition for spoken dialogue systems.” in INTER … Shevade, SS Keerthi, C. Bhattacharyya, and KRK Murthy, “Improvements to the smo algorithm for svm regression,” Neural Networks, IEEE Transactions …

Emotional arousal estimation while reading comics based on physiological signal analysis
M Matsubara, O Augereau, CL Sanches… – Proceedings of the 1st …, 2016 – dl.acm.org
… Haag et al. [6] have ap- plied a neural network technique to … [6] A. Haag, S. Goronzy, P. Schaich, and J. Williams. Emotion recognition using bio-sensors: First steps towards an automatic system. In Tutorial and research workshop on affective dialogue systems, pages 36–48. …

“Modeling citizens’ urban time-use using adaptive hypermedia surveys to obtain an urban planning, citizen-centric, methodological reinvention”
ML Marsal-Llacuna, R Fabregat-Gesa – Time & Society, 2016 – journals.sagepub.com”,
,,

Large Scale Data Enabled Evolution of Spoken Language Research and Applications
S Jothilakshmi, VN Gudivada – Handbook of Statistics, 2016 – Elsevier
Natural Language Processing (NLP) is an interdisciplinary field whose goal is to analyze and understand human languages. Natural languages are used in two forms.

Mobile conversational agents for context-aware care applications
D Griol, Z Callejas – Cognitive Computation, 2016 – Springer
… To do this, a statistical dialog model based on neural networks is generated taking into account the contextual information and the … The term dialog system denotes a wide range of systems, from simple information systems to complex problem-solving and reasoning applications …

Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera
J Bao, Y Jia, Y Cheng, H Tang, N Xi – Sensors, 2016 – mdpi.com
… Schwarz et al. [13] extracted object features using transfer learning from deep convolutional neural networks in order to recognize … [21] proposed to refine the model of a complex 3D scene through combining state-of-the-art computer vision and a natural dialog system. Sun et al. …

Grammar Is a System That Characterizes Talk in Interaction
J Ginzburg, M Poesio – Frontiers in Psychology, 2016 – ncbi.nlm.nih.gov
… Finally, we suggest that grammar formalisms covering such phenomena would provide a better foundation not just for linguistic analysis of face-to-face interaction, but also for sister disciplines, such as research on spoken dialogue systems and/or psychological work on …

Multi-Objective Heuristic Feature Selection for Speech-Based Multilingual Emotion Recognition
C Brester, E Semenkin, M Sidorov – Journal of Artificial Intelligence …, 2016 – degruyter.com
… tion Problem One of the obvious ways to improve the intel- lectual abilities of spoken dialogue systems is that related to their personalization. … A feedforward neural network with one hidden layer is trained with the error backpropagation algorithm (BP). …

Improvement opportunities in commodity trucks delivery in globalized markets
I Makarova, R Khabibullin, E Belyaev… – … -stru?ni ?asopis za more i …, 2016 – hrcak.srce.hr
… search in the database, precedent-based reasoning, simulation modeling, evolutionary computation and genetic algorithms [17], neural networks, situational analysis … 2. A dialogue system allowing the user to enquire which data should be selected and how they should be …

How Challenging is Sarcasm versus Irony Classification?: An Analysis From Human and Computational Perspectives
A Joshi, V Tripathi, P Bhattacharyya, MJ Carman… – Australasian Language … – aclweb.org
… 2016. A deeper look into sarcas- tic tweets using deep convolutional neural networks. arXiv preprint arXiv: 1610.08815. … 2006.” yeah right”: sarcasm recog- nition for spoken dialogue systems. Byron C Wallace and Laura Kertz Do Kook Choe. 2014. …

New Insights into Turbo-Decoding-Based AVSR with Dynamic StreamWeights
S Gergen, S Zeiler, AH Abdelaziz… – … ; 12. ITG Symposium; …, 2016 – ieeexplore.ieee.org
… In this next step, deep neural networks or regression stream weight estimation will be utilized with the goal to enable highly … turbo- decoding weighted forward-backward algorithm for multi- modal speech recognition,” in 5th International Workshop on Spoken Dialog Systems, pp. …

Towards an Hybrid Approach for Semantic Arabic Spontaneous Speech Analysis
C Lhioui, A Zouaghi, M Zrigui – rcs.cic.ipn.mx
… them we can mention: context-sensitive grammar [16], cases grammar [11], Hidden Markov Models [9], Neural Networks [28], N-gram … In the same context, [25] used also a Bayesian stochastic approach to speech semantic composition in Human/Machine dialogue systems. …

Extraction of Speech Parameters from Speech Database using Festival
SN Kayte, M Mundada – Extraction, 2016 – pdfs.semanticscholar.org
… Speech synthesis systems can be extremely useful to people who are visually challenged, visually impaired and illiterate to get into the mainstream society. More recent applications include spoken dialogue systems and communicative robots. …

Classifying Emotions in Customer Support Dialogues in Social Media
J Herzig, G Feigenblat, M Shmueli-Scheuer… – 17th Annual Meeting of …, 2016 – aclweb.org
… technique. 2.2 Emotion Expression Prediction The works in (Skowron, 2010) and (D’Mello et al., 2009) presented dialogue systems that sense the user emotions, such that the system further op- timizes its affect response. Both …

A survey: Cyber-physical-social systems and their system-level design methodology
J Zeng, LT Yang, M Lin, H Ning, J Ma – Future Generation Computer …, 2016 – Elsevier
The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environ.

“A Review of 40 Years of Cognitive Architecture Research: Focus on Perception, Attention, Learning and Applications”
I Kotseruba, OJA Gonzalez, JK Tsotsos – arXiv preprint arXiv:1610.08602, 2016 – arxiv.org
… architectures? At present, some of the most publicized achievements of deep learning include vision processing for self-driving cars (Mobileye5) and Google’s neural networks capable of playing Go [32] and multiple video games [33]. …

Towards the Experimental Studies of a Web-based Platform for Eco-Tourism
D Halvatzaras, K Kabassi – ijres.org
Page 1. International Journal of Research in Engineering and Science (IJRES) ISSN (Online): 2320-9364, ISSN (Print): 2320-9356 www.ijres.org Volume 4 Issue 3 ? March. 2016 ? PP.35-49 www.ijres.org 35 | Page Towards …

2016 Index IEEE Signal Processing Magazine Vol. 33
T Abhayapala, M Aburdene, A Acero… – IEEE SIgnal …, 2016 – ieeexplore.ieee.org
… 2016 4-5 Ward, R., Meeting the Needs of Our Members [President’s Message]; MSP Nov. 2016 4-5 Weng, F., Angkititrakul, P., Shriberg, E., Heck, L., Peters, S., and Hansen, J., Conversational In-Vehicle Dialog Systems: The past, present, and future; MSP Nov. …

Fuzzy system to adapt web voice interfaces dynamically in a vehicle sensor tracking application definition
G Cueva-Fernandez, JP Espada, V García-Díaz… – Soft Computing, 2016 – Springer
… For example, artificial neural networks can be used to infer the distraction level of a driver and adjust the interface depending on the context, or fuzzy logic could be used to give accurate results as an inference mechanisms for in-vehicle communication systems (Tchankue et al. …

Modeling dynamics of expressive body gestures in dyadic interactions
Z Yang, S Narayanan – IEEE Transactions on Affective …, 2016 – ieeexplore.ieee.org
… expressions and speech cues [20]. To consider the temporal dynamics of non- acted body gestures, a Recurrent Neural Network algorithm was employed for emotion recognition in the context of a video game [21]. In spite of the …

Did You Say U2 or YouTube?: Inferring Implicit Transcripts from Voice Search Logs
M Shokouhi, U Ozertem, N Craswell – Proceedings of the 25th …, 2016 – dl.acm.org
Page 1. Did You Say U2 or YouTube? Inferring Implicit Transcripts from Voice Search Logs Milad Shokouhi Microsoft milads@microsoft.com Umut Ozertem Microsoft umuto@microsoft.com Nick Craswell Microsoft nickcr@microsoft.com …

Did you say U2 or Youtube?
M Shokouhi, U Ozertem, N Craswell – microsoft.com
Page 1. Did you say U2 or Youtube? Inferring Implicit Transcripts from Voice Search Logs Milad Shokouhi Microsoft milads@microsoft.com Umut Ozertem Microsoft umuto@microsoft.com Nick Craswell Microsoft nickcr@microsoft.com …

A survey on data-driven approaches in educational games
D Hooshyar, C Lee, H Lim – Science in Information Technology …, 2016 – ieeexplore.ieee.org
… In the research of Yannakakis et al. [35], Artificial Neural Networks (ANNs) were conditioned to predict a computer-controlled physical game’s entertainingness. … [26] DJ Litman and S, “Pan. Designing and evaluating an adaptive spoken dialogue system”. …

Extraction of sparse features of color images in recognizing objects
TTQ Bui, TT Vu, KS Hong – International Journal of Control, …, 2016 – search.proquest.com
… 70, no. 5, pp. 1885-1898, 1993. [37] A. Turnip and K.-S. Hong Classifying mental activities from EEG-P300 signals using adaptive neural network, International Journal of … His research interests include language understanding, computer vision, dialog system, and robotics. …

“Modification of energy spectra, epoch parameters and prosody for emotion conversion in speech”
A Haque, KS Rao – International Journal of Speech Technology, 2016 – Springer
… 2008). A recent work was also undertaken on prosodic mapping using neural networks for emotion conversion in Hindi (Yadav and Rao 2016). A majority of the existing works were based on modi- fication of only prosody. One …

Internet of things for remote elderly monitoring: a study from user-centered perspective
I Azimi, AM Rahmani, P Liljeberg… – Journal of Ambient …, 2016 – Springer”,
,,

Character Modeling through Dialogue for Expressive Natural Language Generation,
,G Lin – 2016 – escholarship.org,
,”… 12 Page 27. 2015b], was created to build end-to-end dialogue systems with recurrent neural networks (RNN) and n-gram models. It contains dialogue of 3 turns between two interlocutors, which restricts the modeling data to dialogue with only two speakers. …

Hybrid recognition technology for isolated voice commands
G Bartiši?t?, K Ratkevi?ius, G Paškauskait? – … Systems Architecture and …, 2016 – Springer
… most popular are Bayesian (Naive Bayes NB), the nearest neighbor (K-Nearest Neighbour kNN), decision tree (Decision Tree), multilayered neural network (Multilayer Perceptron … 1. Suendermann, D., Pieraccini, R.: SLU in commercial and research spoken dialogue systems. …

Artificial Persuasion in Pedagogical Games
Z Zeng – arXiv preprint arXiv:1601.06245, 2016 – arxiv.org
… 45 Figure 5.7 Example of a Persuasion Cue ….. 46 Figure 5.8 Water Molecule Requests Teaching from the Student ….. 46 Figure 5.9 Example Dialogue Entry in Unity Dialogue System ….. 48 …

Situated Intelligent Interactive Systems,
,Z Yu – 2016 – cs.cmu.edu,
,”… Page 3. Abstract The recent wide usage of Interactive Systems (or Dialog Systems), such as Apple Siri has at- tracted a lot of attention. The ultimate goal is to transform current systems into real intelligent … 14 4 TickTock, A Non-Task-Oriented Dialog System Framework 15 …

Agentní p?ístup k dialogovému ?ízení,
,T Nestorovi? – 2016 – otik.uk.zcu.cz,
,”… modal dialogue systems; adopted from [Lee10]. Page 20. 6 a set of rules that describe possible sequences of words and produce a corresponding symbolic representation. In contrast, the stochastical approaches [Kon09] use for this procedure either neural networks or Hidden …

NEWS & HIGHLIGHTS
G FUNDING, DG LECTURERS – pdfs.semanticscholar.org
… Malone $1,201,385 DELIANG WANG Air Force Research Laboratory Deep neural networks for speech separation with application to robust speech recognition 09/26/2015 – 09/25/2017 PI: D. Wang $149,998 HUAMIN WANG …

“Vocal Interactivity in-and-between Humans, Animals, and Robots”
RK Moore, R Marxer, S Thill – Frontiers in Robotics and AI, 2016 – eprints.whiterose.ac.uk
Page 1. This is an author produced version of Vocal Interactivity in and between Humans, Animals, and Robots. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/108727/ Article: Moore, RK, Marxer …

Learning to Interpret and Generate Instructional Recipes,
,C Kiddon – 2016 – digital.lib.washington.edu,
,”… 14 2.4 Concept-to-Text Natural Language Generation . . . . . 16 2.5 Neural Networks for Natural Language Tasks . . . . . 17 2.6 Previous Approaches to Recipe Generation . . … 69 4.5 Dialogue System Results . . . . . …

Vision-based engagement detection in Virtual Reality
G Tofighi, H Gu, K Raahemifar – Digital Media Industry & …, 2016 – ieeexplore.ieee.org
… Mota used both neural networks for posture detection and Hidden Markov Models to detect engagement at an overall accuracy … Stent, Patrick Ehlen, Marilyn Walker, Steve Whittaker, and Preetam Maloor, “”Match: An architecture for multimodal dialogue systems,”” in Proceedings …

Emotion Identification from Spontaneous Communication,
,M Kebede – 2016 – etd.aau.edu.et,
,”… An optimal feature set is selected through the use of generic algorithm and used to train Multilayer Perceptron Neural Network (MLPNN) classifier. … Feature extraction; Feature selection; Classifier; Multilayer Perceptron Neural Network Page 4. Acknowledgments …

Towards Building A Domain Agnostic Natural Language Interface to Real-World Relational Databases
SH Ramesh, J Jain, KS Sarath… – … Conference on Natural …, 2016 – aclweb.org
… Word representations computed using neural networks are especially interesting because the learned vec- tors explicitly encode many linguistic regularities and patterns. … support for complex SQL queries like nested queries and we also plan to make it a dialog system that is …

Grounding the detection of the user’s likes and dislikes on the topic structure of human-agent interactions
C Langlet, C Clavel – Knowledge-Based Systems, 2016 – Elsevier
… The system embeds linguistic resources such as lexicons, dependency grammars and dialogue information provided by the dialogue system. … define semantic rules grounded on the hierarchic relations within the sentences, in [17], they train a recursive neural network on a …

Incrementally resolving references in order to identify visually present objects in a situated dialogue setting,
,C Kennington – 2016 – pub.uni-bielefeld.de,
,”… The practical goal of such a model is for it to be implemented as a component for use in a live, interactive, autonomous spoken dialogue system. … Page 4. 4 Acknowledgements Upon arrival at Bielefeld, my knowledge about dialogue systems research was impoverished at best. …

Agent-Oriented Methodology for Designing Cognitive Agents for Serious Games
CW Shiang, JJ Meyer, K Taveter – Engineering Multi-Agent Systems – utdmavs.org
… with the player through different learning algorithms like statistical analysis, machine learning, and learning algorithms based on neural network reinforcement [4 … Goal-based communication using BDI agents as virtual humans in training: An ontology driven dialogue system. …

Linguistic structure emerges through the interaction of memory constraints and communicative pressures
ML Lewis, MC Frank – Behavioral and Brain Sciences, 2016 – stanford.edu
… uous mapping models. Journal of Memory and Language 38:419–39. [aMHC] Altmann, GTM (2002) Learning and development in neural networks: The im- portance of prior experience. Cognition 85:43–50. [aMHC, MLD] Altmann …

The power of online genetic algorithm in stealth assessment for school readiness
I Suleiman, M Arslan, R Alhajj, M Ridley – Journal of Computers in …, 2016 – Springer
… (1985), Goldberg and Richardson (1987), Scheduling, Cleveland and Smith (1989), Syswerda and Palmucci (1991), evolving neural network design, Harp et al. (1989), Miller et al. (1989), evolving computer programmes, Fogel et al. …

Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM
Y Li, H Hu, Y Wen, J Zhang – arXiv preprint arXiv:1608.04171, 2016 – arxiv.org
… In this paper, we propose a novel distance measurement and build a time series classification algorithm hybridizing nearest neighbour and long short term memory (LSTM) neural network. … The hybrid al- gorithm is based on a well trained LSTM neural network. …

Evaluating the Conversational Interface
M McTear, Z Callejas, D Griol – The Conversational Interface, 2016 – Springer
… The PARADISE framework has also been enhanced to enable the evaluation of multimodal dialog systems. … In order to do so, they used different models including linear regression, decision trees, and neural networks. Similarly, Yang et al. …

STRESS RECOGNITION FROM SPEECH SIGNAL
M STAN?K – vutbr.cz,
,”… signal processing, speech signal processing, emotion recognition, psychological stress, formant, vowel polygons, glottal flow analysis, glottal pulse, Return-To-Opening phase ratio, Closing-To-Opening phase ratio, COG shift, classifiers, neural networks, Gaussian Mixture …

Acquisition of multimodal data corpus for automatic sign language processing
J Ga?ka, P W?grzynowicz, M M?sior – Studia Informatica, 2016 – studiainformatica.polsl.pl
… (which is enough for building a simple automatic dialogue system). In each session, a single sign was repeated multiple times. Table 1 … Classification scores obtained using models trained on different data streams were fused using the Artificial Neural Network (ANN) …

Sentiment analysis: from opinion mining to human-agent interaction
C Clavel, Z Callejas – IEEE Transactions on affective computing, 2016 – ieeexplore.ieee.org
… Detection and avoidance of user frustration in driving situations [77] or for tutoring systems [78] or for a child conversational computer game [83]. Detection of various emotions according to the application for dialog systems [25], [81], [83]. …

The conversational interface
M McTear, Z Callejas, D Griol – 2016 – Springer
… With the evolution of speech recognition and natural language technologies, IVR systems rapidly became more sophisticated and enabled the creation of complex dialog systems that could handle natural language queries and many turns of interaction. …

Fuzzy context-specific intention inference for robotic caregiving
R Liu, X Zhang – International Journal of Advanced Robotic …, 2016 – journals.sagepub.com”,
,,

CITATIONS TO THE WORK OF
WJ Rapaport – 2016 – cse.buffalo.edu,
,”Page 1. CITATIONS TO THE WORK OF William J. Rapaport Department of Computer Science and Engineering Department of Philosophy, Department of Linguistics, and Center for Cognitive Science State University of New York at Buffalo, Buffalo, NY 14260-2500 …

Automatic sarcasm detection: A survey
A Joshi, P Bhattacharyya, MJ Carman – arXiv preprint arXiv:1602.03426, 2016 – arxiv.org
… They report an improvement of 2% in per- formance. Ghosh and Veale [2016] use a combination of convolutional neural network, LSTM followed by a DNN. … Silvio Amir et al. [2016] capture author-specific embeddings for a neural network based architecture. …

Searching by talking: Analysis of voice queries on mobile web search
I Guy – Proceedings of the 39th International ACM SIGIR …, 2016 – dl.acm.org
… [38] defined voice search as “the technology underlying many spoken dialog systems that provide users with the in- formation they request with a spoken query”, and reviewed key challenges, such as environmental noise, pronunciation variance, and linguistic issues. …

A real-time audio-to-audio karaoke generation system for monaural recordings based on singing voice suppression and key conversion techniques
H Tachibana, Y Mizuno, N Ono… – Journal of Information …, 2016 – jstage.jst.go.jp
Page 1. Journal of Information Processing Vol.24 No.3 470–482 (May 2016) [DOI: 10.2197/ipsjjip.24.470] Regular Paper A Real-time Audio-to-audio Karaoke Generation System for Monaural Recordings Based on Singing Voice Suppression and Key Conversion Techniques …

“Modeling Personality, Mood, and Emotions”
J Zhang, J Zheng, N Magnenat-Thalmann – Context Aware Human-Robot …, 2016 – Springer”,
,,

A Study on Dialogue Agent Adapting to Various Situations
A Jordan – 2016 – 133.87.26.249,
,”Page 1. Instructions for use Title A Study on Dialogue Agent Adapting to Various Situations Author(s) Jordan, Arnaud Citation Issue Date 2016-03-24 DOI Doc URL http://hdl.handle. net/2115/61751 Right Type theses (doctoral) Additional Information …

Extending MAM5 Meta-Model and JaCalIV E Framework to Integrate Smart Devices from Real Environments
JA Rincon, JL Poza-Lujan, V Julian… – PloS one, 2016 – journals.plos.org
This paper presents the extension of a meta-model ( MAM 5) and a framework based on the model ( JaCalIVE ) for developing intelligent virtual environments. The goal of this extension is to develop augmented mirror worlds that represent a real and virtual world coupled, so that …

Energy-scalable speech recognition circuits
M Price – 2016 – dspace.mit.edu,
,”… TC typical-case TDNN time-delay neural network TSMC Taiwan Semiconductor Manufacturing Company UBM universal background model UPF Unified Power Format USB Universal Serial Bus UVM Universal Verification Methodology … In dialogue systems, the …

The Effect of Narrow-Band Transmission on Recognition of Paralinguistic Information From Human Vocalizations
S Frühholz, E Marchi, B Schuller – IEEE Access, 2016 – ieeexplore.ieee.org
Page 1. Received June 30, 2016, accepted July 27, 2016, date of publication August 29, 2016, date of current version October 15, 2016. Digital Object Identifier 10.1109/ACCESS.2016.2604038 The Effect of Narrow-Band Transmission on …

A Base Camp for Scaling AI
CJC Burges, T Hart, Z Yang, S Cucerzan… – arXiv preprint arXiv: …, 2016 – arxiv.org
… Large neural network models are powerful but are becoming increasingly complex. … An effective automated open domain dialog system will likely require a rich world model and the ability to perform commonsense reasoning over it [14]. …

Discriminative Training of Linear Transformations and Mixture Density Splitting for Speech Recognition
MSMA Tahir – researchgate.net,
,”… 6.3 Discriminative Splitting for Deep Neural Networks . … a higher level task; for example language under- standing [Bender & Macherey + 03], speech to speech translation [Bub & Schwinn 96], spoken document retrieval [Johnson & Jourlin + 99], spoken dialog system [Kristiina & …

Robust spoken language understanding for house service robots
A Vanzo, D Croce, E Bastianelli, R Basili, D Nardi – Polibits, 2016 – polibits.cidetec.ipn.mx
… rahman Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, TSG Dahl, and B. Kingsbury, “Deep neural networks for acoustic … Traum, and S. Narayanan, “A reranking approach for recognition and classification of speech input in conversational dialogue systems,” in Spoken …

Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics
P Wiriyathammabhum, D Summers-Stay… – ACM Computing …, 2016 – dl.acm.org
… For multitask learning, Hand and Chellappa [2016] depicted an improved concept of attributes, where features in different convolutional neural networks are shared based on word relation. … 2013] or Neural Networks [Socher et al. 2014]. …

Contributions to the joint segmentation and classification of sequences (My two cents on decoding and handwriting recognition)
SE Boquera – 2016 – riunet.upv.es,
,”… offline, bimodal). We have proposed some novel preprocessing techniques for offline HTR which replace classical ge- ometrical heuristics and make use of automatic learning techniques (neural networks). Experiments conducted …

Towards building a review recommendation system that trains novices by leveraging the actions of experts
S Khanal – 2016 – digitalcommons.unl.edu,
,Page 1. University of Nebraska – Lincoln DigitalCommons@University of Nebraska – Lincoln …,

“Department of Information Technology, Uppsala University, Sweden”
A Folkesson – 2016 – it.uu.se,
,”… (fulltext ). Feature augmented deep neural networks for segmentation of cells . Sajith Kecheril Sadanandan, Petter Ranefall, and Carolina Wählby. … (fulltext , preview image ). Toward incremental dialogue act segmentation in fast-paced interactive dialogue systems . …

Multilingvální rozpoznáva? telefonní ?e?i na bázi DNN-HMM
J Fiala – 2016 – dspace.cvut.cz,
,”… types of spoken recordings. In general, the speech recognition covers the area from simple commands recognition to the sophisticated dialog systems and artificial personal assistants. … In the recent years, the ANN (Artificial Neural Networks) have provided noticeable results. …

Predictive Incremental Parsing Helps Language Modeling
A Köhn, T Baumann – aclweb.org
… Second, our approach is applicable to online decoding for incremental systems such as highly interactive spoken dialogue systems, whereas other methods are often limited to post-hoc lattice rescoring. 3 Predictive Parsing …

Semi-supervised and unsupervised methods for categorizing posts in web discussion forums
K Perumal – arXiv preprint arXiv:1604.00119, 2016 – arxiv.org
… Other unsupervised techniques have been employed for the related tasks of dialogue act classification in spoken dialogue systems (Crook et al., 2009) and Twitter conversations (Ritter et al., 2010). Although they worked specifically on genres of text that are very …

Military Usages of Speech and Language Technologies: A Review
D GRIOL, J GARCÍA-HERRERO… – … Through Data Analytics …, 2016 – books.google.com
… access remote information. For this reason, spoken dialog systems (SDS)[1, 2, 3] are becoming a strong alternative to traditional graphical interfaces, which might not be appropriate for all users and/or applications. These systems …

Referential Choice: Predictability and Its Limits
AA Kibrik, MV Khudyakova, GB Dobrov… – Frontiers in …, 2016 – ncbi.nlm.nih.gov
… Grüning and Kibrik (2005) applied the neural networks method of machine learning to the same small dataset as in Kibrik (1999); that study showed that machine learning is in principle appropriate for modeling multi-factorial referential choice and raised the question of creating …

Giving eyesight to the blind: Towards attention-aware AIED
SK D’Mello – International Journal of Artificial Intelligence in …, 2016 – Springer
… distracted. For example, the use of the learner’s first name (eg, BMary, what do you think about this problem^) in spoken dialog systems should be effective in capturing attention a la the cocktail-party effect (Cherry 1953). Of …

A review study of human-affection knowledge on usability engineering
D Lakshmi, R Ponnusamy – Advances in Human Machine …, 2016 – ieeexplore.ieee.org
… Introducing CURRENNT – the Munich Open-Source CUDA Recurrent Neural Network Toolkit. … Sensors 2014, 14, 17491–17515. [50] Alonso-Martín, F.; Castro-González, A.; Luengo, F.; Salichs, M. Augmented Robotics Dialog System for Enhancing Human–Robot Interaction. …

Glove-based virtual hand grasping for virtual mechanical assembly
JR Li, JR Li, YH Xu, YH Xu, JL Ni, JL Ni… – Assembly …, 2016 – emeraldinsight.com
… assembly automation (Chen et al., 2015a). Luzanin and Plancak (2014) proposed a hand gesture recognition approach using a cluster-trained probabilistic neural network. Thus, it has been widely studied. Thanks to the virtual hand …

Imitation learning for language generation from unaligned data
G Lampouras, A Vlachos – aclweb.org
Page 1. Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1101–1112, Osaka, Japan, December 11-17 2016. Imitation learning for language generation from unaligned data …

Speech Recognition Using Articulatory and Excitation Source
KS Rao, KE Manjunath – Springer
… Some of the topics covered in this series include the presentation of real life commercial deployment of spoken dialog systems, contemporary methods of speech … The articulatory features (AFs) are derived from the spectral features using feedforward neural networks (FFNNs). …

Data Formats for Emotion and Personality
MASN Nunes, J Granatyr – meninasnacomputacao.com.br
Page 1. Data Formats for Emotion and Personality Maria Augusta SN Nunes1 and Jones Granatyr2 Abstract: Data formats used in the representation of affective aspects are usually formed by different computational formalisms …

Application requirements for Robotic Nursing Assistants in hospital environments
S Cremer, K Doelling… – SPIE …, 2016 – proceedings.spiedigitallibrary.org
… Ideally, a dialog system … The idea is to set the desired interface mapping parameters “by learning” utilizing online neuro-adaptive control algorithms with two Neural Networks (one for the “Actor” that generates the interface mapping, and one for the “Critic”, which approximates …

A flowchart-based intelligent tutoring system model to improve students’ problem-solving skills/Danial Hooshyar
H Danial – 2016 – studentsrepo.um.edu.my,
,”Page 1. A FLOWCHART-BASED INTELLIGENT TUTORING SYSTEM MODEL TO IMPROVE STUDENTS’ PROBLEM- SOLVING SKILLS DANIAL HOOSHYAR FACULTY OF COMPUTER SCIENCE AND INFORMATION TECHNOLOGY UNIVERSITY OF MALAYA …

Designing Human-Centered Collective Intelligence
I Addo – 2016 – epublications.marquette.edu,
,”… 22 D. Motivational Interviewing in CI dialogue systems ….. 23 … animal-like embodiment. I contrast some of the differences between the proposed approach and that of other prominent affective HRI dialog systems in Table I. …

Cross-cultural computing: an artist’s journey
N Tosa – 2016 – Springer,
,”… A festival called “Artificial Life” took place at Ars Electronica in 1993. Studies and artworks used computers and robots aimed at simulating lifelike evolution or humanization by using theories behind biological and neural networks. …

“Emotion Recognition from Speech with Acoustic, Non-Linear and Wavelet-based Features Extracted in Different Acoustic Conditions”
JC Vásquez Correa – 2016 – bibliotecadigital.udea.edu.co,
,”… Page 20. 2 Introduction efforts are devoted to increasing accessibility and efficiency of spoken dialogue systems by integrating emotional and other paralinguistic cues [2]. There are a great deal of potential application that may use technologies related to …

A simple interaction model for learner agents: An evolutionary approach
A Beigi, N Mozayani – Journal of Intelligent & Fuzzy Systems, 2016 – content.iospress.com
… A good example of such systems is an artificial neural network composed of a large number of neurons. … 42–50. [5]. Cuayahuitl H. , Hierarchical Reinforcement Learning for Spoken Dialogue Systems, PhD Thesis, University of Edinburgh, (2009) . [6]. …

Knowledge transfer by sharing acoustic-model parameters for automatic speech recognition
AK Mohan – 2016 – digitool.library.mcgill.ca,
,”… For this I would like to acknowledge Dr. Umesh and the entire consortium associated with developing dialogue systems for Indian farmers. … I would also like to acknowledge Dr. Yun Tang, for sharing his knowledge on neural network acoustic modelling for ASR. …

Model-free learning on robot kinematic chains using a nested multi-agent topology
JN Karigiannis, CS Tzafestas – Journal of Experimental & …, 2016 – Taylor & Francis
… Data-driven methods for adaptive spoken dialogue systems: Computational learning for conversational interfaces … Press.), we have seen cases where single agent architectures employ RL methods in a continuous three-dimensional (3D) space, implemented by neural networks. …

Evolutionary Algorithms under Noise and Uncertainty: a location-allocation case study
M Vallejo, DW Corne – 2016 – macs.hw.ac.uk
… This is the case of the use of Artificial Neural Networks (ANN) as a modelling tool for function approximation [30], [31 … The same gathering method was successfully applied in dialogue systems [68], environmental studies [69] or emulators for managing uncertainty in urban …

Incremental Learning from Scratch Using Analogical Reasoning
V Letard, S Rosset, G Illouz – Tools with Artificial Intelligence ( …, 2016 – ieeexplore.ieee.org
Page 1. Incremental Learning From Scratch Using Analogical Reasoning Vincent Letard1,2,3, Sophie Rosset1 and Gabriel Illouz1,2,3 1LIMSI CNRS, France 2Université Paris Sud, France 3Université Paris Saclay, France firstname.lastname@limsi.fr …

Ontology-Based Semantic Q&A system in Health Care: An Illustrated
SH Wang – 2016 – etd.lib.nsysu.edu.tw,
,”Page 1. ???????????? ???? Department of Information Management National Sun Yat-sen University Master Thesis ???????????????????? Ontology-Based Semantic Q&A system in Health Care: An Illustrated …

Sign transition modeling and a scalable solution to continuous sign language recognition for real-world applications
K Li, Z Zhou, CH Lee – ACM Transactions on Accessible Computing ( …, 2016 – dl.acm.org
Page 1. 7 Sign Transition Modeling and a Scalable Solution to Continuous Sign Language Recognition for Real-World Applications KEHUANG LI, Georgia Institute of Technology ZHENGYU ZHOU, Research and Technology …

The Essence of Smart Homes: Application of Intelligent Technologies
A Ghaffarianhoseini, A Ghaffarianhoseini… – Artificial Intelligence: …, 2016 – books.google.com
Page 110. 79 Chapter 4 The Essence of Smart Homes: Application of Intelligent Technologies towards Smarter Urban Future Amirhosein Ghaffarianhoseini University of Malaya (UM), Malaysia Ali Ghaffarianhoseini Auckland …

Characterization of an Extrapolation Chamber for Dosimetry of Low Energy X-Ray Beams
FM Bastos, TA da Silva – Sustainable Development, 2016 – waset.org”,
,Conferences.,

A Study on the Effect of Design Factors of Slim Keyboard’s Tactile Feedback
KC Lin, CF Wu, HL Hsu, YH Tu, CC Wu – Topology, 2016 – waset.org
… 283. 10003704. Particle Filter Supported with the Neural Network for Aircraft Tracking Based on Kernel and Active Contour. …

Extending the Peak Bandwidth of Parameters for Softmax Selection in Reinforcement Learning
K Iwata – IEEE transactions on neural networks and learning …, 2016 – ieeexplore.ieee.org
… Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 … 2 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS actions, and rewards, expressed as …

Providing arguments in discussions on the basis of the prediction of human argumentative behavior
A Rosenfeld, S Kraus – ACM Transactions on Interactive Intelligent …, 2016 – dl.acm.org
… An agent can help a user in this task by revealing additional 3Also known as dialog systems. ACM Transactions on Interactive Intelligent Systems, Vol. 6, No. 4, Article 30, Publication date: December 2016. Page 6. 30:6 A. Rosenfeld and S. Kraus …

“Intelligent Robotics and Applications: 9th International Conference, ICIRA 2016, Tokyo, Japan, August 22-24, 2016, Proceedings”
N Kubota, K Kiguchi, H Liu, T Obo – 2016 – books.google.com
… Human Action Recognition . . . . . 237 Zhi Chao Li, Ryad Chellali, and Yi Yang Page 18. Contents – Part I XIX A Study on Classification of Food Texture with Recurrent Neural Network. . . . Shuhei Okada, Hiroyuki …

Information Fusion Approaches for Distant Speech Recognition in a Multi-microphone Setting
CMG Flores – 2016 – researchgate.net,
,”… BF Beamforming CS Channel Selection DIRHA Distant-Speech Interaction for Robust Home Applications DNN Deep Neural Network DSR Distant Speech Recognition EV Envelope Variance HMM Hidden Markov Model IR Impulse Response LM Language Model …

Exploiting Semantic and Topic Context to Improve Recognition of Proper Names in Diachronic Audio Documents
I Sheikh – 2016 – hal.archives-ouvertes.fr,
,”… Probabilistic topic models and word embeddings from neural network models are explored for the task of retrieval of relevant proper names. … Neural network context models trained with an objective to maximise the retrieval per- formance are proposed. …

Statistical task modeling of activities of daily living for rehabilitation
ÉMD Jean-Baptiste – 2016 – etheses.bham.ac.uk,
,”… 12 1.1.3.4 Cue Generation Module . . . . . 14 1.1.4 Similarities with a Spoken Dialogue System . . . . . 14 1.2 Contribution . . . . . … 22 2.2.2 Contention Scheduling . . . . . 24 2.2.3 Neural Network . . . . . …

An Analysis of Student Model Portability
BV Aguirre, JAR Uresti, B Du Boulay – International Journal of Artificial …, 2016 – Springer”,
,,

Language-Independent Methods for Computer-Assisted Pronunciation Training
A Lee – 2016 – groups.csail.mit.edu,
,”… In addition, motivated by the success of deep learning models in unsupervised feature learning, we explore the use of convolutional neural networks (CNNs) for mispronunciation detection. … 6.1.2 Convolutional Neural Network (CNN) . . . . 97 …

Analysis and estimation of driver visual attention using head position and orientation in naturalistic driving conditions
S Jha – 2016 – search.proquest.com,
,”… et al., 2013). It can also play an important role in in-vehicle situated dialog systems (Misu, 2015). In a controlled … of the driver. These systems can also be helpful in enhancing in-car dialog systems (Misu, 2015). In human-computer …

EEG-based emotion recognition of Quran listeners
A Fattouh, I Albidewi, B Baterfi – Computing for Sustainable …, 2016 – ieeexplore.ieee.org
… T. Vogt, E. André, and N. Bee, 9EmoVoice2A framework for online recognition of emotions from voice,9 in Perception in multimodal dialogue systems, ed: Springer … [17] N. Fragopanagos and JG Taylor, 9Emotion recognition in human- computer interaction,9 Neural Networks, vol. …

“VLEs, social stories and children with autism: A prototype implementation and evaluation”
C Volioti, T Tsiatsos, S Mavropoulou… – Education and …, 2016 – Springer”,
,,

A study of the use of natural language processing for conversational agents
RS Wilkens – 2016 – lume.ufrgs.br,
,”… by several terms, including animated agents or embedded conversational agents (ECA). A dialogue system has six basic components. (1) The speech recognition component is res- … In this sense dialogue systems provide a natural language interaction with the user. …

Prosody Utilization in Continuous Speech Recognition
J Bartošek – 2016 – dspace.cvut.cz,
,”… 33 2.5.2 Dialogue systems and personal assistants . . . . . 33 2.6 Goals of the thesis . . . . . … 71 7.4.2 Neural Networks for Temporal Processing . . . . . 71 7.4.3 Training and Testing Data . . . . . …

Unsupervised entity linking using graph-based semantic similarity
AM Naderi – 2016 – upcommons.upc.edu,
,”… developed in a way that reflects the innate ability provided by the brain’s neural networks. However, there still exist the moments that the text disambiguation task would remain a … EL systems can be used in the platform of all human-computer/robot dialogue systems. …

“Natural Language Processing and Computational Linguistics: Speech, Morphology and Syntax”
MZ Kurdi – 2016 – books.google.com,
,”… 215 4.4.6. Tabular parsing (chart). . . . 221 4.4.7. Probabilistic parsing . . . . . 225 4.4.8. Neural networks. . . . . 233 4.4.9. parsing algorithms for unification-based grammars . . . . . …

Discriminative Acoustic Features for Deployable Speech Recognition
A Faria – 2016 – eecs.berkeley.edu,
,”… This work fits in a broader re- search agenda that is currently popular: deep learning using deep neural networks (DNN), a term that has gained nearly mainstream adoption. … 3If the reader prefers, please interpret MLP as “feedforward neural network, possibly shallow” Page 16. …

Intelligent Conversational Bot for Massive Online Open Courses (MOOCs)
SL Lim, OS Goh – arXiv preprint arXiv:1601.07065, 2016 – arxiv.org
… Among these works are multimodal human-robot interaction with AIML chatter bot system (Tan et. al., 2012), and Artificial Intelligent Neural-network Identity (AINI) architecture (Goh, et. al., 2007) in human-computer communication system. …

Complex multidisciplinary systems decomposition for aerospace vehicle conceptual design and technology acquisition
A Omoragbon – 2016 – search.proquest.com,
,”… MVO MultiVariate Optimisation RAE Farnborough Aircraft 1973. 8. NEURAL NETWORK FORMULATION. Optimization method for Aircrat Design Georgia Institute of. … Vehicle 1986-. TsAGI Dialog System for Preliminary Design TsAGI. VASCOMPII V/STOL Aircraft Sizing and. …

Linguistic Knowledge in Data-Driven Natural Language Processing
Y Tsvetkov – 2016 – cs.cmu.edu,
,”… Mikolov et al., 2010); lexicalized concepts and relations are now encoded in a hierarchy of abstract representations in a neural network. … in Russian and Persian, to grammatically parse Cantonese, to model Latin or Hebrew morphology, to build dialog systems for indigenous …

A data mining approach to ontology learning for automatic content-related question-answering in MOOCs.
S Shatnawi – 2016 – openair.rgu.ac.uk,
,”… 26 2.7.3 Pattern (Rule)-based Classifiers . . . . . 27 2.7.4 Support Vector Machines (SVM) Classifiers . . . . . 27 2.7.5 Neural Network Classifiers . . . . . 28 2.8 Frequent Pattern Mining . . . . . …

Applying Distributed Cognition for Safer Hospital Technology
M HUSSAIN – 2016 – academia.edu,
,”Page 1. Applying Distributed Cognition for Safer Hospital Technology Master’s Thesis By MUSTAFA HUSSAIN Advisor Dr. Anas Salah Eddin Internal Faculty Committee Dr. James Dewey Internal Faculty Dr. Harvey Hyman Library of Congress Dr. Nadir Weibel UC San Diego …

Thulasizwe Innocent Mashiyane
J Adeyemo – 2016 – ir.dut.ac.za,
,”… ANFIS Adaptive Neuro-Fuzzy Inference System ANN Artificial Neural Network BCS Best Compromise Solution … 2014). Common examples of DDMs are statistical frequency distribution models, artificial neural networks (Komma, Blöschl and Reszler 2008; Kumar, Raghuwanshi …

1st International Workshop on Multimodal Media Data Analytics (MMDA 2016)
S Vrochidis, M Melero, L Wanner, J Grivolla, Y Estève… – ecai2016.org
Page 1. ECAI 2016, MMDA 2016 workshop, August 2016 1st International Workshop on Multimodal Media Data Analytics (MMDA 2016) The rapid advancements of digital technologies, as well as the penetration of internet and …

i-Vector modeling of speech attributes for automatic foreign accent recognition
H Behravan, V Hautamaki… – … on Audio, Speech, …, 2016 – ieeexplore.ieee.org
… Each detector is realized with three single hidden layer feed-forward ANNs (artificial neural networks) or- ganized in a hierarchical structure and trained on sub-band en- ergy trajectories extracted through 15-band mel-frequency fil- terbank. For each critical band, a window of …

From predictive to interactive multimodal language learning
A Lazaridou – 2016 – eprints-phd.biblio.unitn.it,
,”… 87 5-2 Neural network of player A1. In this particular game, A1 produces the … Traditionally, computational models of meaning relying on corpus-extracted context vectors, such as LSA [57], HAL [62], Topic Models [37] and more recent neural-network approaches [20, …

Persuasive Teachable Agent for Intergenerational Learning
SF Lim – arXiv preprint arXiv:1601.07264, 2016 – arxiv.org
Page 1. Persuasive Teachable Agent for Intergenerational Learning [A Book Draft] By Su Fang Lim 27 Jan 2016 Page 2. i Abstract Teachable agents are computer agents based on the pedagogical concept of learning-by- teaching. …

Parameter estimation of Japanese predicate argument structure analysis model using eye gaze information
R Maki, H Nishikawa… – Proceedings the 26th …, 2016 – pdfs.semanticscholar.org
… The eye gaze information has attracted much attention in various NLP tasks in recent years such as dialogue systems (Prasov et al., 2007; Qu and Chai, 2007), reference resolution (Prasov and … Neural network-based model for Japanese predicate argument structure analysis. …

Computer?Supported Collaborative Decision?Making
FG Filip, CB Zamfirescu, C Ciurea – … collaboration, & e-services (ISSN 2193 …, 2016 – Springer
… The document contained several original results, such as agent-based social simulation for group decisions, swarming models of computa- tion to automate the facilitation of group decisions, goal-oriented dialog system with inconsistent knowledge bases and so on. …

Learning Open Domain Knowledge From Text
GG Angeli – 2016 – nlp.stanford.edu,
,”Page 1. LEARNING OPEN DOMAIN KNOWLEDGE FROM TEXT A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY …

Personality in Argumentative Agents
ME Etheredge – 2016 – dspace.library.uu.nl,
,”… of applications. In the field of multi-agent systems dialogue systems are studied in terms of different types of dialogues, rules for participation and contribution in the dialogue and the role of arguments in the dialogue. Walton …

Making sense: from word distribution to meaning
E Santus – 2016 – ira.lib.polyu.edu.hk,
,”Page 1. Copyright Undertaking This thesis is protected by copyright, with all rights reserved. By reading and using the thesis, the reader understands and agrees to the following terms: 1. The reader will abide by the rules and …

A Pattern-Based Approach for Sarcasm Detection on Twitter
M Bouazizi, TO Ohtsuki – IEEE Access, 2016 – ieeexplore.ieee.org
Page 1. Received June 22, 2016, accepted July 14, 2016, date of publication August 24, 2016, date of current version September 28, 2016. Digital Object Identifier 10.1109/ACCESS. 2016.2594194 A Pattern-Based Approach for Sarcasm Detection on Twitter …

Computational methods in semantics
G Recski – 2016 – nytud.hu,
,”Page 1. Computational Methods in Semantics Gábor Recski Ph.D. Dissertation Supervisor: András Kornai D.Sc. Ph.D. School of Linguistics Gábor Tolcsvai Nagy MHAS Theoretical Linguistics Ph.D Program Zoltán Bánréti C.Sc. Department of Theoretical Linguistics …

Metaphor: A computational perspective
T Veale, E Shutova… – Synthesis Lectures on …, 2016 – morganclaypool.com
… Semantic Role Labeling Martha Palmer, Daniel Gildea, and Nianwen Xue 2010 Spoken Dialogue Systems Kristiina Jokinen and Michael McTear 2009 Introduction to Chinese Natural Language Processing Kam-Fai Wong, Wenjie Li, Ruifeng Xu, and Zheng-sheng Zhang 2009 …

Efficiency analysis of verbal radio communication in air combat simulation
H Lilja – 2016 – diva-portal.org,
,”… A self-organizing map (SOM) is an artificial neural network (ANN) which is trained in an unsupervised way through competitive learning. … Typical efficiency metrics used to evaluate dialogue systems are system turns, user turns and elapsed time [19]. …

Sequential decisions and predictions in natural language processing
H He – 2016 – search.proquest.com,
,”… For example, a convolutional neural network can be used for object recognition where predictions are object classes; a maximum entropy classier can be used for text classication where predictions are text labels such as topics or sentiment. …

Metaheuristic applications to speech enhancement
P Kunche, K Reddy – SpringerBriefs in electrical and computer engineering …, 2016 – Springer
… Some of the topics covered in this series include the presentation of real life commercial deployment of spoken dialog systems, contemporary methods of speech parameterization, developments in information security for automated speech, forensic speaker recognition, use …

Complex multidisciplinary system composition for aerospace vehicle conceptual design
L Gonzalez – 2016 – search.proquest.com,
,”Complex multidisciplinary system composition for aerospace vehicle conceptual design. Abstract. Although, there exists a vast amount of work concerning the analysis, design, integration of aerospace vehicle systems, there …

Leveraging biometric data to boost software developer productivity
T Fritz, SC Müller – Software Analysis, Evolution, and …, 2016 – ieeexplore.ieee.org
Page 1. Leveraging Biometric Data to Boost Software Developer Productivity Thomas Fritz and Sebastian C. Müller Department of Informatics University of Zurich, Switzerland Email: 1fritz,smuellerl@ifi.uzh.ch Abstract—Producing …

Linguistic Linked Open Data
D Trandab??, D Gîfu – Springer
Page 1. 123 Diana Trandab?? Daniela Gîfu (Eds.) 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop Sibiu, Romania, July 13–25, 2015 Revised Selected Papers Linguistic Linked Open Data Communications in Computer and Information Science 588 Page 2. …

“Linguistic Linked Open Data: 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop, Sibiu, Romania, July 13-25, 2015, Revised Selected …”
TD Maria, D Gîfu – 2016 – books.google.com
Page 1. Diana Trandab?? Daniela Gîfu (Eds.) Communications in Computer and Information Science 588 Linguistic Linked Open Data 12th EUROLAN 2015 Summer School and RUMOUR 2015 Workshop Sibiu, Romania, July 13–25, 2015 Revised Selected Papers 123 Page 2. …

Postfilters to modify the modulation spectrum for statistical parametric speech synthesis
S Takamichi, T Toda, AW Black, G Neubig… – IEEE/ACM Transactions …, 2016 – dl.acm.org
Page 1. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 24, NO. 4, APRIL 2016 755 Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis Shinnosuke …

Social Sensorimotor Contingencies
J Bütepage – 2016 – diva-portal.org,
,”… Therefore, it is of advantage to study the human mind and its biological basis when aiming at creating human-like intelligence. Artificial neural networks – an example Biologically … Let us for instance examine ar- tificial neural networks. While …

Data-driven natural language generation using statistical machine translation and discriminative learning
E Manishina – 2016 – theses.fr,
,”… Most modern systems communicating directly with the user share one common feature: they have a dialog system (DS) at their base. As of … Chapter 1 Introduction 1.1 Dialog systems The humanity has long been passionate about creating intelligent machines that can freely …

Analysis and modeling for robust whispered speech recognition
S Ghaffarzadegan – 2016 – search.proquest.com,
,”… Finally, a sampling strategy from Gaussian mixture models is proposed in the last chapter of this dissertation to explore the prospects of a deep neural network (DNN) to capture the data structure from a small dataset. … DCT Discrete Cosine Transform. DNN Deep Neural Network. …

“Formative Assessment, Learning Data Analytics and Gamification: In ICT Education”
S Caballé, R Clarisó – 2016 – books.google.com
… Work….. 309 References ….. 310 Conversational Agents as Learning Facilitators: Experiences With a Mobile Multimodal Dialogue System Architecture….. 313 …

Capturing and Animating Hand and Finger Motion for 3D Communicative Characters
NS Wheatland – 2016 – escholarship.org,
,Page 1. …,

Taking Stock of the Cross?Linguistic Data: Spatial Frames of Reference and Their Effect on Thought
L ABARBANELL, P LI – spatial.ucsb.edu
Page 1. 2016 Specialist Meeting—Universals and Variation in Spatial Referencing Abarbanell and Li—1 Taking Stock of the Cross?Linguistic Data: Spatial Frames of Reference and Their Effect on Thought LINDA ABARBANELL …

Movement Data
S Hoogervorst – pure.tue.nl,
,”Page 1. / Department of Computer Science Predicting Website Visitor Gender and Age with Mouse Movement Data Stijn Hoogervorst Wednesday 31st August, 2016 Predicting Website Visitor Gender and Age with Mouse Movement Data Stijn Hoogervorst …

“The Internet of Things supporting the Cultural Heritage domain: analysis, design and implementation of a smart framework enhancing the smartness of cultural spaces”
F Piccialli – 2016 – fedoa.unina.it,
,”Page 1. Universit`a degli Studi di Napoli Federico II Dipartimento di Matematica e Applicazioni “Renato Caccioppoli” Ph.D. Thesis in Scienze Computazionali e Informatiche – XXVIII Ciclo The Internet of Things supporting the Cultural Heritage domain: analysis, design and …

Using Linguistic Knowledge for Improving Automatic Speech Recognition Accuracy in Air Traffic Control
VN Nguyen – 2016 – brage.bibsys.no,
,”Page 1. Using Linguistic Knowledge for Improving Automatic Speech Recognition Accuracy in Air Traffic Control Master’s Thesis in Computer Science Van Nhan Nguyen May 18, 2016 Halden, Norway Page 2. Page 3. Abstract …

Department of Computer Science & Engineering Revised Syllabi (Detailed) for B. Tech in Computer Science and Engineering (2010 Admission onwards)
CSFOF COMPUTING – cse.nitc.ac.in,
,”Page 1. Department of Computer Science & Engineering Revised Syllabi (Detailed) for BTech in Computer Science and Engineering (2010 Admission onwards) CS1001 FOUNDATIONS OF COMPUTING Pre-requisite: NIL CORE LTPC 2 0 0 2 Course Outcomes: …

Chain Graphs
D Sonntag – 2016 – liu.diva-portal.org,
,”Page 1. Linköping Studies in Science and Technology Dissertations. No. 1748 Chain Graphs – Interpretations, Expressiveness and Learning Algorithms by Dag Sonntag Department of Computer and Information Science Linköping University SE-581 83 Linköping, Sweden …

Constructing a language from scratch: combining bottom-up and top-down learning processes in a computational model of language acquisition
J Gaspers, P Cimiano, K Rohlfing… – IEEE Transactions on …, 2016 – ieeexplore.ieee.org
… Starting from the written, normalized data, ASR errors were simulated roughly following Jung et al. [50]. The authors presented a system for user simulation which can be utilized to evaluate spoken dialogue systems, and the system also includes ASR channel simulation. …

Referential choice
AA Kibrik, MV Khudyakova, GB Dobrov, A Linnik… – 2016 – publishup.uni-potsdam.de
… Grüning and Kibrik (2005) applied the neural networks method of machine learning to the same small dataset as in Kibrik (1999); that study showed that machine learning is in principle appropriate for modeling multi-factorial referential choice and raised the question of creating …

Language Modeling for Automatic Speech Recognition of Inflective Languages: An Applications-Oriented Approach Using Lexical Data
G Donaj, Z Ka?i? – 2016 – books.google.com
… The development of speech technologies enables a more natural way of interaction with computers systems. Several technologies are needed for such an interaction: speech recognition, dialog systems and speech synthesis. …

A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation
A Bilgin, H Hagras, J van Helvert… – IEEE Transactions on …, 2016 – ieeexplore.ieee.org
Page 1. 306 IEEE TRANSACTIONS ON FUZZY SYSTEMS, VOL. 24, NO. 2, APRIL 2016 A Linear General Type-2 Fuzzy-Logic-Based Computing With Words Approach for Realizing an Ambient Intelligent Platform for Cooking Recipe Recommendation …

Integration of a Portfolio-based Approach to Evaluate Aerospace R and D Problem Formulation Into a Parametric Synthesis Tool
AR Oza – 2016 – search.proquest.com,
,”Page 1. Integration of a Portfolio-based Approach to Evaluate Aerospace R&D Problem Formulation Into a Parametric Synthesis Tool by AMIT R. OZA Presented to the Faculty of the Graduate School of The University of Texas at Arlington in Partial Fulfillment …

Bridging the Gap Between Imitation Learning and Inverse Reinforcement Learning
B Piot, M Geist, O Pietquin – … transactions on neural networks …, 2016 – ieeexplore.ieee.org
… Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Bridging the Gap Between Imitation Learning … 2 IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS …

Grounding robot motion in natural language and visual perception
SA Bronikowski – 2016 – search.proquest.com,
,”… evaluation scheme. This manuscript, entitled Driving Under the Inuence (of Language), was submitted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS) on 26 January 2016. It is currently in review. I …

Principles of Noology
SB Ho – Springer,
,”… and psychological models of emotions, bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning etc.). … 129 3.8.4 The Computational Power of Recurrent Neural Networks . . . . …

Modeling multiple time series annotations based on ground truth inference and distortion
R Gupta, K Audhkhasi, Z Jacokes… – IEEE Transactions …, 2016 – ieeexplore.ieee.org
Page 1. 1949-3045 (c) 2016 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …

Principles of Noology: Toward a Theory and Science of Intelligence
SB Ho – 2016 – books.google.com,
,”… computational and psychological models of emotions, bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning … 3.8.4 The Computational Power of Recurrent Neural Networks . …

Supportive behaviors for human-robot teaming
B Hayes – 2016 – search.proquest.com,
,”Supportive Behaviors for Human-Robot Teaming. Abstract. While robotics has made considerable strides toward more robust and adaptive manipulation, perception, and planning, robots in the near future are unlikely to be as …

Reading Faces. Using Hard Multi-Task Metric Learning for Kernel Regression
J Nicolle – 2016 – theses.fr,
,”… ‘CompanionAble’1 led to Hector, a robot designed for assisting elderly people living alone. Among other abilities, it includes a personalized dialog system displaying emotional intelli- gence to avoid feelings of loneliness and offer cognitive stimulation through games. …

Lithuanian Broadcast Speech Transcription Using Semi-Supervised Acoustic Model Training}}
R Lileikyte, A Gorin, L Lamel, JL Gauvain… – SLTU 2016, 2016 – limsi.fr
… icassp = “”Proceedings of ICASSP””} @STRING{ieeeicassp = “”Proceedings of the IEEE-ICASSP””} @STRING{ieeeasr = “”Proceeding of IEEE Workshop on Automatic Speech Recognition””} @STRING{ijcnn = “”IEEE joint conference on neural networks””} @STRING{eurospeech …

Large-scale affective computing for visual multimedia
B Jou – 2016 – search.proquest.com,
,”… And in Chapter 6, we propose a new learning setting called cross-residual learning building o recent successes in deep neural networks, and specically, in residual learning; we show that cross-residual learning can be used e ectively to jointly learn across even multiple related …

Automatic Processing of Text Responses in Large-Scale Assessments
F Zehner – 2016 – mediatum.ub.tum.de,
,”Page 1. TECHNISCHE UNIVERSITÄT MÜNCHEN Lehrstuhl für Empirische Bildungsforschung Zentrum für Internationale Bildungsvergleichsstudien (ZIB) eV Automatic Processing of Text Responses in Large-Scale Assessments Fabian Zehner …

Generation of textual summaries at different target reading levels: summarizing line graphs for visually impaired users
PS Moraes – 2016 – search.proquest.com,
,”Generation of textual summaries at different target reading levels: Summarizing line graphs for visually impaired users. Abstract. This work is concerned with the generation of text at different reading levels by tailoring the generated …

3.35 Open Problems and State-of-Art of Session Types
N Yoshida – Compositional Verification Methods for Next- …, 2016 – pdfs.semanticscholar.org
Page 24. 22 15191–Compositional Verification Methods for Next-Generation Concurrency 3.35 Open Problems and State-of-Art of Session Types Nobuko Yoshida (Imperial College London, GB) License Creative Commons …

Self-reported symptoms of depression and PTSD are associated with reduced vowel space in screening interviews
S Scherer, GM Lucas, J Gratch… – IEEE Transactions …, 2016 – ieeexplore.ieee.org
… participants with symptoms of depression or PTSD show a significantly reduced opening phase of the vocal fold vibration

Automatic text and speech processing for the detection of dementia
K Fraser – 2016 – search.proquest.com,
,”Automatic text and speech processing for the detection of dementia. Abstract. Dementia is a gradual cognitive decline that typically occurs as a consequence of neurodegenerative disease, and can result in language deficits (ie, aphasia). …