CNN (Convolutional Neural Network) & Dialog Systems 2016


Notes:

From 2014 to 2015 the number of papers on CNNs in dialog systems in tripled.

  • Deep neural networks (DNNs)

Wikipedia:

See also:

100 Best GitHub: Sentence Boundary | Sentence Boundary Disambiguation & Dialog Systems | Sentence Extraction | Sentence Extraction Module | Sentence Extractor | Sentence Generation Module | Sentence Grammaticality | Sentence Parsers & Dialog Systems | Sentence Patterns & Dialog Systems | Sentence Planner | Sentence Recognition | Sentence Splitter 2011 | Sentence Splitting & Dialog Systems | Sentence Summarization


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
… [3] M. Gašic, C. Breslin, M. Henderson, D. Kim, M. Szummer, B. Thomson, P. Tsiakoulis, and S. Young. On-line policy optimisation of bayesian spoken dialogue systems via human interaction. In ICASSP, 2013. … A convolutional neural network for modelling sentences. ACL, 2014. …

Sequential short-text classification with recurrent and convolutional neural networks
JY Lee, F Dernoncourt – arXiv preprint arXiv:1603.03827, 2016 – arxiv.org
… Several recent studies using ANNs have shown promising results, including convolutional neural networks (CNNs) (Kim, 2014; Blunsom et al., 2014; Kalchbrenner et al., 2014) and recursive neural net- works … In 7th International Workshop on Spoken Dialogue Systems (IWSDS …

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
… which was the 2015 CoNLL shared task (Xue et al., 2015); and dialog act clas- sification, which characterizes the structure of in- terpersonal communication in the Switchboard cor- pus (Stolcke et al., 2000), and is a key component of contemporary dialog systems (Williams and …

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
… We use a bi-directional recurrent neural network to propagate information across words; a convolutional neural network layer further captures patterns of adjacent words. Then a matching layer combines the information in each individual sentence. …

Key-value memory networks for directly reading documents
A Miller, A Fisch, J Dodge, AH Karimi, A Bordes… – arXiv preprint arXiv …, 2016 – arxiv.org
… Even though standard pipeline QA systems like AskMR (Banko et al., 2002) have been recently re- visited (Tsai et al., 2015), the best published results on TRECQA and WIKIQA have been obtained by either convolutional neural networks (Santos et al., 2016; Yin and Schütze …

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 … Our second model, which we term NBT-CNN, draws inspiration from successful applications of Convolutional Neural Networks (CNNs) for …

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
… Using a joint model for the two SLU tasks simplifies the dialog system, as only one model needs to be trained and deployed. … IEEE, 2014, pp. 554–559. [9] P. Xu and R. Sarikaya, “Convolutional neural network based triangular crf for joint intent detection and slot filling,” in Au …

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
… This has impli- cations for a wide variety of NL applications like question answering, sentence completion, paraphrase generation, and next utterance prediction in dialog systems. … This can be used in various applications in dialog systems, eg, intent modeling. …

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
… [8] Giuseppe Di Fabbrizio, Gokhan Tur, and Dilek Hakkani-Tür, “Bootstrapping spoken dialog systems with data reuse … [19] Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Grégoire Mesnil, “Learning semantic representations using convolutional neural networks for web …

Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM.
D Hakkani-Tür, G Tür, A Celikyilmaz… – …, 2016 – pdfs.semanticscholar.org
… [21] P. Xu and R. Sarikaya, “Convolutional neural network based tri … 3, March 2015. [40] T.-H. Wen, M. Gasic, N. Mrksic, P.-H. Su, D. Vandyke, and S. Young, “Semantically conditioned LSTM-based natural lan- guage generation for spoken dialogue systems,” arXiv preprint arXiv …

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 …

A neural knowledge language model
S Ahn, H Choi, T Pärnamaa, Y Bengio – arXiv preprint arXiv:1608.00318, 2016 – arxiv.org
… understanding. Beyond its usage as a standalone application, 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]. …

End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding.
YN Chen, D Hakkani-Tür, G Tür, J Gao… – …, 2016 – pdfs.semanticscholar.org
… In the past decades, goal-oriented spoken dialogue systems (SDS) are being incorporated in various devices and allow users to speak to systems in order to finish tasks more efficiently, for example, the virtual … 13] P. Xu and R. Sarikaya, “Convolutional neural network based trian …

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
… This is sometimes called “perforated” upsam- pling in the context of convolutional neural networks (CNNs). It was first demonstrated to work well in Dosovitskiy et al. … Building end-to-end dialogue systems using generative hierarchical neural network models. …

Overview of the NTCIR-12 Short Text Conversation Task.
L Shang, T Sakai, Z Lu, H Li, R Higashinaka… – …, 2016 – pdfs.semanticscholar.org
… 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. …

Knowledge as a teacher: Knowledge-guided structural attention networks
YN Chen, D Hakkani-Tur, G Tur, A Celikyilmaz… – arXiv preprint arXiv …, 2016 – arxiv.org
… 1 Introduction In the past decade, goal-oriented spoken dialogue systems (SDS), such as the virtual personal assis- … Page 5. rent neural networks (RNN), and 3) convolutional neural networks (CNN) with a window size 3 and a max-pooling operation. …

Deep Learning of Audio and Language Features for Humor Prediction.
D Bertero, P Fung – LREC, 2016 – lrec-conf.org
… 2.5. Convolutional Neural Network (CNN) The CNN is another kind of neural network useful to en- code a linear or multidimensional structure such a … Our ultimate goal is to integrate laughter response prediction in a machine dialog system, to allow it to understand and react to …

Multi-view Response Selection for Human-Computer Conversation.
X Zhou, D Dong, H Wu, S Zhao, D Yu, H Tian, X Liu… – EMNLP, 2016 – aclweb.org
… Lu and Li (2013) proposed a DNN-based matching model for response selection. Hu et al., (2014) improved the performance using Convolutional Neural Networks (CNN) (LeCun et al., 1989). In 2015, a further study conducted by Wang et al. …

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. … classifiers can be applied, such as support vector machines (SVMs) (Haffner et al., 2003) and convolutional neural networks (CNNs) (Xu and …

Sequence-level knowledge distillation
Y Kim, AM Rush – arXiv preprint arXiv:1606.07947, 2016 – arxiv.org
Page 1. Sequence-Level Knowledge Distillation Yoon Kim yoonkim@seas.harvard. edu Alexander M. Rush srush@seas.harvard.edu School of Engineering and Applied Sciences Harvard University Cambridge, MA, USA Abstract …

Syntax or semantics? knowledge-guided joint semantic frame parsing
YN Chen, D Hakanni-Tür, G Tur… – … (SLT), 2016 IEEE, 2016 – ieeexplore.ieee.org
… In the past decade, goal-oriented spoken dialogue systems (SDS), such as the virtual personal assistants Microsoft’s Cortana and Ap … We apply convolutional neural networks (CNN) with a window size 3 and a max-pooling operation for knowledge encoding models, CNNkg and …

Deep reinforcement learning with an action space defined by natural language
J He, J Chen, X He, J Gao, L Li, L Deng, M Ostendorf – 2016 – openreview.net
… Mnih et al., 2015). In the DQN, a deep convolutional neural network is used to extract high- level features from images, which are further mapped into Q-function values of different actions via a linear transform. In Atari games …

A Sentence Interaction Network for Modeling Dependence between Sentences.
B Liu, M Huang, S Liu, X Zhu, X Zhu – ACL (1), 2016 – aclweb.org
… Kim (2014) proposed a Convolutional Neural Network (CNN) for sentence classification which models a sentence in multiple granularities. … Yin et al. (2015) proposed an Attention based Convolutional Neural Network (ABCNN) for sentence pair mod- eling. …

Exploring Convolutional and Recurrent Neural Networks in Sequential Labelling for Dialogue Topic Tracking.
S Kim, RE Banchs, H Li – ACL (1), 2016 – aclweb.org
… This paper presents various ar- tificial neural network models for dialogue topic tracking, including convolutional neural networks to account for … These human capabilities for handling topics are also expected from dialogue systems to achieve natural and human-like conver …

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. …

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 … Deep convolutional neural networks have enabled us to learn higher-level image representa- tions and distinguish among the 1000 ImageNet …

Natural language generation in dialogue using lexicalized and delexicalized data
S Sharma, J He, K Suleman, H Schulz… – arXiv preprint arXiv …, 2016 – arxiv.org
… Donahue et al. (2015) processed per-frame features from convolutional neural networks using LSTM-RNNs to generate descriptions of videos. … For task-oriented dialogue systems, Wen et al. (2015b) used RNNs to learn to generate delexical- ized responses from dialogue-acts. …

Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition.
P Fung, A Dey, FB Siddique, R Lin… – COLING …, 2016 – 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 …

Dialogue Act Classification in Domain-Independent Conversations Using a Deep Recurrent Neural Network.
H Khanpour, N Guntakandla, R Nielsen – COLING, 2016 – aclweb.org
… Many applications benefit from the use of automatic dialogue act classi- fication such as dialogue systems, machine translation, Automatic … Kalchbrenner and Blun- som (2013) used a mixture of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). …

Optimizing neural network hyperparameters with gaussian processes for dialog act classification
F Dernoncourt, JY Lee – Spoken Language Technology …, 2016 – ieeexplore.ieee.org
… systematic approach based on Bayesian optimization with Gaussian process (GP) [16] has been shown to be effective in automatically tuning the hyperparameters of machine learning algorithms, such as latent dirichlet allo- cation, SVMs, convolutional neural networks [15], and …

Topic detection and tracking for conversational content by using conceptual dynamic latent Dirichlet allocation
JF Yeh, YS Tan, CH Lee – Neurocomputing, 2016 – Elsevier
… Wang et al. proposed a word-embedding-based approach for expanding short texts by clustering the convolutional neural network for text classification. … A multidomain conversational dialogue system focuses on processing goal-oriented dialogue and chat [13]. …

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
… 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- level tagging …

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
… 2015. Deep contextual language under- standing in spoken dialogue systems. In Proc. INTER- SPEECH, pages 120–124. … 2013. Convolutional neural network based triangular CRF for joint intent detection and slot filling. In Proc. ASRU, pages 78–83. …

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 …

Neural Networks Learning the Concept of Influence in Go.
GM Santos, RM da Silva Julia, M Saito, MA Aguiar – FLAIRS Conference, 2016 – aaai.org
… Mimicking go experts with convolutional neural networks. In Proceedings of the 18th International Conference on Artificial Neural Net- works, Part II, ICANN ’08, 101–110. … An application of reinforcement learn- ing to dialogue strategy in a spoken dialogue system for email. …

Does IR Need Deep Learning?
H Li – 2016 – hangli-hl.com
… Page 10. Two Magic DL Tools for IR • Convolutional Neural Network (CNN) • Sequence to Sequence Learning (S2SL) … Neural Responding Machine: generation-based single turn dialogue system using deep learning • Model: sequence-to-sequence learning (encoder …

Assisting discussion forum users using deep recurrent neural networks
JHP Suorra, O Mogren – Proceedings of the 1st Workshop on …, 2016 – aclweb.org
… decoder model to perform re- sponse generation from questions as input, and training the model using two posts as input and the following response as target.(Serban et al., 2016) presented a dialog system built as … 2014. A convolutional neural network for modelling sentences. …

A hierarchical lstm model for joint tasks
Q Zhou, L Wen, X Wang, L Ma, Y Wang – China National Conference on …, 2016 – Springer
… word segmentation and POS-tagging, POS-tagging and chunking, intent identification and slot filling in goal-driven spoken language dialogue systems, and so on. … [8] proposed a hybrid model of Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) which …

Conversational Engagement Recognition Using Auditory and Visual Cues.
Y Huang, E Gilmartin, N Campbell – INTERSPEECH, 2016 – researchgate.net
… A convolutional neural network based analy- sis was seen to be the most effective. … This model would have a wide range of appli- cations in areas including spoken dialogue systems, event de- tection in videos of conversations, detection of user satisfaction when using …

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 …

Recent Advances on Human-Computer Dialogue
X Wang, C Yuan – CAAI Transactions on Intelligence Technology, 2016 – Elsevier
… Thirdly, interdependences of subtasks in dialogue systems make online adaptation of systems challenging. … Xu, & Sarikaya [40] described a joint model for intent detection and slot filling based on convolutional neural networks (CNN). …

Driver confusion status detection using recurrent neural networks
C Hori, S Watanabe, T Hori… – Multimedia and Expo …, 2016 – ieeexplore.ieee.org
… neural networks (RNNs) and related architectures such as and long short-term memory (LSTM) RNNs, and convolutional neural networks (CNNs) have … [2] Teruhisa Misu, Antoine Raux, Ian Lane, Joan Devassy, and Rakesh Gupta, “Situated multi-modal dialog system in vehicles …

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. … The first model we use is the Convolutional Neural Network [9], which is useful to obtain a fixed-length vector representation of an utterance, an audio signal or an im- age. …

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. …

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. … Lai, S., Xu, L., Liu, K., & Zhao, J. (2015, January).Recurrent Convolutional Neural Networks for Text Classification.In …

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
… In natu- ral language processing, reinforcement learning has been applied successfully to dialogue systems that generate natural language and converse with a hu- man user (Scheffler and Young, 2002; Singh et al., 1999; Wen et al., 2016). …

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
Page 1. Medical Examination Data Prediction Using Simple Recurrent Network and Long Short-Term Memory Han-Gyu Kim School of Computing Korea Advanced Institute of Science and Technology 291 Daehak-ro, Yuseong …

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 …

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
… response to gestural cues [7]. In one example, pointing and gaze behaviors were recognized in an imitative game using a hidden Markov model [8]. Data-driven dialogue systems have been … used in conjunction with convolutional neural networks for question-answering tasks[28 …

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
Page 1. Transfer Learning for Cross-Lingual Sentiment Classification with Weakly Shared Deep Neural Networks Guangyou Zhou1, Zhao Zeng1, Jimmy Xiangji Huang2, and Tingting He1 1 School of Computer, Central China …

Deep learning for sentiment analysis
LM Rojas?Barahona – Language and Linguistics Compass, 2016 – Wiley Online Library
… 3.3.2 Convolutional neural network. … A convolutional neural network with three hidden units on Layer l that belong to the same feature map. Arrows with the same color represent shared weights in the activation. 3.4 Initialization of input vectors (word embeddings). …

Improving Attention Modeling with Implicit Distortion and Fertility for Machine Translation.
S Feng, S Liu, N Yang, M Li, M Zhou, KQ Zhu – COLING, 2016 – aclweb.org
… 2015. Abc-cnn: An attention based convolutional neural network for visual question answering. arXiv preprint arXiv:1511.05960. … 2015. Se- mantically conditioned lstm-based natural language generation for spoken dialogue systems. arXiv preprint arXiv:1508.01745. …

Knowledge-Based Semantic Embedding for Machine Translation
C Shi, S Liu, S Ren, S Feng, M Li, M Zhou… – Proceedings of the …, 2016 – aclanthology.info
… rant and Liang, 2014). In (Yih et al., 2015), with a deep convolutional neural network (CNN), the question sentence is mapped into a query graph, based on which the answer is searched in knowl- edge base. In our paper, we …

A Joint Model of Intent Determination and Slot Filling for Spoken Language Understanding.
X Zhang, H Wang – IJCAI, 2016 – pdfs.semanticscholar.org
… Spoken language understanding (SLU) in human/machine spoken dialog systems aims to automatically identify the in- tent of the user as … for joint work on ID and SF, Xu and Sarikaya [2013] improved the triangular CRF model by us- ing convolutional neural networks (CNNs) to …

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 …

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
… the development of a deep learning methodology for semantic utterance classification (SUC) for use in domain-specific dialogue systems. … the semantic class and the word sequence in an utterance, we have proposed a shallow convolutional neural network (CNN) along with a …

Reading Comprehension using Entity-based Memory Network
X Wang, K Sudoh, M Nagata, T Shibata… – arXiv preprint arXiv …, 2016 – arxiv.org
… Zhang, X., Bordes, A., Chopra, S., Miller, A., Szlam, A., Weston, J.: Evaluating prerequisite qualities for learning end-to-end dialog systems. … 9(8), 1735– 1780 (1997) 6. Johnson, R., Zhang, T.: Effective use of word order for text categorization with convolutional neural networks. …

TEXT NORMALIZATION FOR AUTOMATIC SPEECH RECOGNITION SYSTEMS
AF VASILE, T BORO? – Editors: Maria Mitrofan Daniela Gîfu Dan Tufi? … – consilr.info.uaic.ro
… Bang, J., Park, S., Lee, GG (2015). ASR Independent Hybrid Recurrent Neural Network Based Error Correction for Dialog System Applications. In … Lai, S., Xu, L., Liu, K., Zhao, J.(2015). Recurrent Convolutional Neural Networks for Text Classification. In AAAI, 2267-2273. …

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
… neural networks for sentence classification. In EMNLP, 2014. [7] J. Pennington, R. Socher, and CD Manning. 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 …

Neural dialog state tracker for large ontologies by attention mechanism
Y Jang, J Ham, BJ Lee, Y Chang… – … Workshop (SLT), 2016 …, 2016 – ieeexplore.ieee.org
… 9, pp. 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. …

Towards an end to end Dynamic Dialogue System
V Bhalla – researchgate.net
… The closest work by Williams et.al. [7] provides an interactive learning based graphical user interface (GUI) to rapidly scale dialogue systems that uses binary classifiers to propose labels. Tur et.al. … The convolutional neural network clustering on short texts by Xu et.al. …

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 …

Neural Networks for Natural Language Processing
L Mou – sei.pku.edu.cn
… [19] Lili Mou, Ge Li, Lu Zhang, Tao Wang, Zhi Jin. “Convolutional neural networks over tree structures for programming language processing.” In AAAI, pages 12871293, 2016. … Motivation: We don’t have the ground truth In a dialogue system, “The nature of of opendomain …

Knowledge Enhanced Hybrid Neural Network for Text Matching
Y Wu, W Wu, Z Li, M Zhou – arXiv preprint arXiv:1611.04684, 2016 – arxiv.org
… enhanced representations. The three channels are processed by a convolutional neural network to generate high level features for matching, and the features are synthe- sized as a matching score by a multilayer perceptron. The …

Multidisciplinary Approaches to Neural Computing
AM Esposito, F Giudicepietro, S Scarpetta, S Khilnani – 2016 – Springer
… for the processing of dynamic signals, in anticipation of the implementation of intelligent avatars, interactive dialog systems, and reliable … 151 Giansalvo Cirrincione, Vincenzo Randazzo and Eros Pasero 16 Convolutional Neural Networks with 3-D Kernels for Voice Activity …

Multimodal deep neural nets for detecting humor in TV sitcoms
D Bertero, P Fung – Spoken Language Technology Workshop …, 2016 – ieeexplore.ieee.org
… Therefore we apply two parallel Convolutional Neural Networks [22] to model each utterance from lower-level features. … instead there was no humorous intent is a very impolite behavior in human interaction, and is not really the desired outcome of an automatic dialog system. …

Ranking Responses Oriented to Conversational Relevance in Chat-bots.
B Wu, B Wang, H Xue – COLING, 2016 – aclweb.org
… 2015. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. arXiv preprint arXiv:1506.08909. … 2015. Encoding source language with convolutional neural network for machine translation. arXiv preprint arXiv:1503.01838. …

Deep Learning for Natural Language Processing-Research at Noah’s Ark Lab
H Li – 2016 – hangli-hl.com
… vectors to represent the meaning of sentences Page 6. Convolutional Neural Network (CNN) • “Robust parsing” • Shared parameter on same level … Alan Turing Page 27. Natural Language Dialogue System – Retrieval based Approach index of messages and responses …

Challenges in Building Highly-Interactive Dialog Systems.
NG Ward, D DeVault – AI Magazine, 2016 – pdfs.semanticscholar.org
Page 1. Challenges in Building Highly-Interactive Dialog Systems Nigel G. Ward* and … tifically significant. We identify key challenges whose solution would lead to improvements in dialog systems and beyond. Introduction Over the …

Multimodal Memory Modelling for Video Captioning
J Wang, W Wang, Y Huang, L Wang, T Tan – arXiv preprint arXiv …, 2016 – arxiv.org
… need long-term dependency modelling, eg, textual ques- tion answering [3, 14], visual question answering [39] and dialog systems [8]. As … In this section, we will first introduce three key com- ponents of our model including: 1) convolutional neural networks (CNN) based video …

Crossmodal Language Grounding, Learning, and Teaching.
S Heinrich, C Weber, S Wermter, R Xie… – CoCo …, 2016 – pdfs.semanticscholar.org
… a major challenge: speech recognition is still limited to good signal-to-noise conditions or well adapted models; dialogue systems depend on … The representation of images and video streams could be inspired by the huge success of convolutional neural networks for a variety of …

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
… U ? Rd×nu and R ? Rd×nr are then used to construct a word-word similarity matrix M1 ? Rnu×nr and a sequence-sequence similarity matrix M2 ? Rnu×nr which are two input chan- nels of a convolutional neural network (CNN). …

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]. …

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. …

CONTEXT MEMORY NETWORKS FOR MULTI-OBJECTIVE SEMANTIC PARSING IN CONVERSATIONAL UNDERSTANDING
A Celikyilmaz, D Hakkani-Tur, G Tur, YN Chen, B Cao… – pdfs.semanticscholar.org
… The Spoken Language Understanding (SLU) in conversa- tional dialog systems parses user utterances into corre- sponding semantic concepts … rather than providing manual features, [4] proposed to au- tomatically learn the features through a convolutional neural networks (CNN …

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
… The HCRN was tested on a spoken dialogue act classification task. The dialogue act (DA) is the communicative intention of a speaker for each sen- tence. Prediction of the DA can be further used as an input to modules in dialogue systems such as dialogue manager. …

A neural network approach for knowledge-driven response generation
P Vougiouklis, J Hare, E Simperl – 2016 – eprints.soton.ac.uk
… Yoon Kim. 2014. Convolutional neural networks for sentence classification. … 2010. Vca: An experiment with a multiparty virtual chat agent. In Proceedings of the 2010 Workshop on Companionable Dialogue Systems, CDS ’10, pages 43–48, Stroudsburg, PA, USA. …

Multilingual Multimodal Language Processing Using Neural Networks
MM Khapra, S Chandar – Proceedings of the 2016 Conference of the …, 2016 – aclweb.org
… 1. Introduction and Motivation [20 mins] 2. Basics [40 mins] 1. Learning distributed representations using Neural Networks 2. Convolutional Neural Networks 3. Recursive Neural Networks and its variants 3. Multilingual/Multimodal Representation Learning [40 … dialog systems. …

Knowledge as a Teacher: Knowledge-Guided Structural Attention Networks
A Celikyilmaz, J Gao, L Deng – researchgate.net
… 1 Introduction In the past decade, goal-oriented spoken dialogue systems (SDS), such as the virtual personal assis- … Page 5. rent neural networks (RNN), and 3) convolutional neural networks (CNN) with a window size 3 and a max-pooling operation. …

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
… humans. Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively. This …

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
… In dialogue systems, eg, long latencies may disrupt the natural turn- taking in the human-machine conversation. … In some cases the context may extend to the whole utterance, eg, in some application of convolutional neural networks; • the decoder that combines the probability …

Towards a corpus of speech emotion for interactive dialog systems
D Bertero, FB Siddique, P Fung – … and Standardization of …, 2016 – ieeexplore.ieee.org
… the Convolutional Neural Network instead we used the model shown in Figure 1 [4]. It is a one filter CNN that takes as input 8kHz raw … together with lack of feature extraction overhead, makes our model very fast at evaluation time and suitable for integration in a dialog system. …

Neural Paraphrase Generation with Stacked Residual LSTM Networks
A Prakash, SA Hasan, K Lee, V Datla, A Qadir… – arXiv preprint arXiv …, 2016 – arxiv.org
… Bahdanau et al., 2015), speech recognition (Li and Wu, 2015), language modeling (Vinyals et al., 2015), and dialogue systems (Serban et al … This is inspired by the recent success of such connections in a deep Convolutional Neural Network (CNN) for the image recognition task …

The splab at the NTCIR-12 Short Text Conversation Task.
K Wu, X Liu, K Yu – NTCIR, 2016 – pdfs.semanticscholar.org
… 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 competition’s organizers. … Learning semantic representations using convolutional neural networks for web search. In Proceedings of the …

Assisting Discussion Forum Users using Deep Recurrent Neural Networks
J Suorra Hagstedt P, O Mogren – Proceedings of the …, 2016 – publications.lib.chalmers.se
… Serban et al., 2016) presented a dialog system built as a hierarchical recurrent LSTM encoder–decoder, where the dia- logue is seen as a sequence of utterances, and each utterance is modelled as a sequence of words. … A convolutional neural network for modelling sentences. …

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]. …

Topic Augmented Neural Network for Short Text Conversation.
Y Wu, W Wu, Z Li, M Zhou – CoRR, 2016 – pdfs.semanticscholar.org
… TANN simulates the process in which people pick important topical concepts according to the input message for responding. We implement the sen- tence embedding layer by a convolutional neural network (CNN) and the matching layer by a multi- layer perceptron (MLP). …

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. …

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 and Microsoft … Modern convolutional neural networks often require billions of operations for a single inference. …

Training an interactive humanoid robot using multimodal deep reinforcement learning
H Cuayáhuitl, G Couly, C Olalainty – arXiv preprint arXiv:1611.08666, 2016 – arxiv.org
… The labelling process is defined as h(x) = argmaxy f(x,y), where f is a scoring function using learnt features x derived from a Convolutional neural network [9]. To train this classifier we use a set of seed labelled … SimpleDS: A simple deep reinforcement learning dialogue system. …

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
… Convolution. We further apply a convolutional neural network (CNN) to extract local neighboring features of successive words— ie, discriminative word sequences can be detected—yielding a more composite representation of the sentences. …

Deep Reinforcement Learning with a Combinatorial Action Space for Predicting and Tracking Popular Discussion Threads
J He, M Ostendorf, X He, J Chen, J Gao, L Li… – CoRR, 2016 – pdfs.semanticscholar.org
… In natu- ral language processing, reinforcement learning has been applied successfully to dialogue systems that generate natural language and converse with a hu- man user (Scheffler and Young, 2002; Singh et al., 1999; Wen et al., 2016). …

Recent Improvements on Error Detection for Automatic Speech Recognition.
Y Estève, S Ghannay, N Camelin – MMDA@ ECAI, 2016 – pdfs.semanticscholar.org
… The training set for the convolutional neural network used to compute acoustic word embedding consists of 488 hours of French … and semantic features for theme identification in telephone conversations’, in 6th International Work- shop on Spoken Dialog Systems (IWSDS 2015 …

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. …

Word representation using a deep neural network
Y Li – 2016 – search.proquest.com
… A deep Convolutional Neural Network (CNN) based image recognition system rst tries to combine the neighboring pixels and identify the … can be used in various scenarios in NLP, such as machine translation, knowledge extraction, information retrieval, and dialogue systems. …

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 …

Dialogue Systems and Dialogue Management
D Burgan – 2016 – dtic.mil
UNCLASSIFIED UNCLASSIFIED Dialogue Systems & Dialogue Management Deeno Burgan National Security & ISR Division Defence Science and Technology Group … Page 3. UNCLASSIFIED UNCLASSIFIED Dialogue Systems & Dialogue Management Executive Summary …

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 …

Addressee and Response Selection for Multi-Party Conversation.
H Ouchi, Y Tsuboi – EMNLP, 2016 – 2boy.org
… Basically, the addressee detection has been tackled in the spoken/multimodal dialog system research, and the models largely rely on acoustic signal or gaze infor- mation (Jovanovic et al., 2006; Akker and Traum, 2009; Ravuri and Stolcke, 2014). …

A Unified Knowledge Representation System for Robot Learning and Dialogue
N Shukla – 2016 – search.proquest.com
… [9] T. Xiao, J. Zhang, K. Yang, Y. Peng, and Z. Zhang, Error-driven incremental learning in deep convolutional neural network for large-scale image classication, in … Collaborative activities and multi-tasking in dialogue systems: Towards natural dialogue with robots. TAL. …

Music Predictions Using Deep Learning. Could LSTM Networks be the New Standard for Collaborative Filtering?
E Keski-Seppälä, M Snellman – 2016 – diva-portal.org
… since it has many hidden layers. ?Source:? ?http://neuralnetworksanddeeplearning.com/chap5. html Figure 2.5:? A GPUbased maxpooling convolutional neural network (GPUMPCNN) won the MICCAI 2013 Grand Challenge on Mitosis Detection. Source: …

Group Sparse CNNs for Question Sentence Classification with Answer Sets
M Ma, L Huang, B Xiang, B Zhou – 2016 – openreview.net
… Question classification has applications in question answering (QA), dialog systems, etc., and has been increasingly popular in recent … For example, several recent efforts employ Convolutional Neural Networks (CNNs) to achieve remarkably strong performance in the TREC …

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 …

Detecting Sarcasm in Multimodal Social Platforms
R Schifanella, P de Juan, J Tetreault… – Proceedings of the 2016 …, 2016 – dl.acm.org
… Specif- ically, the semantics models were built with an off-the-shelf deep convolutional neural network using the Caffe frame- work [14], and the penultimate layer of the convolutional neural network output as the image-feature representation for training classifiers for 1,570 …

A Corpus for Event Localization
C Ward – 2016 – bir.brandeis.edu
Page 1. A Corpus for Event Localization Master’s Thesis Presented to The Faculty of the Graduate School of Arts and Sciences Brandeis University Department of Computer Science Professor James Pustejovsky, Advisor In Partial Fulfillment of the Requirements for the Degree …

Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing
J Su, K Duh, X Carreras – Proceedings of the 2016 Conference on …, 2016 – aclweb.org
… Abstract: As dialog systems become ubiquitous, we must learn how to detect when a system is spoken to, and avoid mistaking … 11:20–11:45 A Position Encoding Convolutional Neural Network Based on Dependency Tree for Relation Classification Yunlun Yang, Yunhai Tong …

Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
K Knight, A Nenkova, O Rambow – … of the 2016 Conference of the North …, 2016 – aclweb.org
… 110 xxi Page 22. Multi-domain Neural Network Language Generation for Spoken Dialogue Systems Tsung-Hsien Wen, Milica Gašic, Nikola Mrkšic, Lina M. Rojas-Barahona, Pei-Hao Su, David Vandyke and Steve Young . . . . . …

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. …

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
K Erk, NA Smith – Proceedings of the 54th Annual Meeting of the …, 2016 – aclanthology.info
Page 1. The 54th Annual Meeting of the Association for Computational Linguistics Proceedings of the Conference, Vol. 1 (Long Papers) August 7-12, 2016 Berlin, Germany Page 2. Platinum Sponsors Gold Sponsors Silver Sponsors ii Page 3. Bronze Sponsors …

Real-time understanding of complex discriminative scene descriptions
R Manuvinakurike, C Kennington, D DeVault… – Proceedings of the 17th …, 2016 – aclweb.org
… 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 …

Real-time language-independent algorithm for dialogue agents
A JORDAN, K ARAKI – ?????, 2016 – jstage.jst.go.jp
… With progress in research and system improve- ments, dialogue agents will be able to handle more and more complicated conversation, for example the Multimodal Multi?domain Spoken Dialogue System?5? has been proposed. …

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 Département d’informatique et de recherche opérationnelle Faculté des arts et des sciences Mémoire …

Exploring the Correlation of Pitch Accents and Semantic Slots for Spoken Language Understanding.
S Stehwien, NT Vu – INTERSPEECH, 2016 – pdfs.semanticscholar.org
… The speech files consist of single utter- ances by speakers requesting flight information from a dialog system, for example Show flights from Burbank to Milwaukee for today. … [32] NT Vu, “Sequential convolutional neural networks for slot fill- ing in spoken language understanding …

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
… hot research topic in speech signal processing field. With the development of computer technologies, the demands for emotion recognition in new spoken dialogue systems are very urgent. It has been proven very useful in many …

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. …

Robust comprehension of natural language instructions by a domestic service robot
T Kobori, T Nakamura, M Nakano, T Nagai… – Advanced …, 2016 – Taylor & Francis
… intro- duced. Xu and Sarikaya [24] proposed a model that in- tegrates dialogue intention classification and slot extrac- tion utilizing features extracted from a user’s utterances by a convolutional neural network (CNN). By utilizing …

PersoNER: Persian Named-Entity Recognition
H Poostchi, E Zare Borzeshi, M Abdous… – The 26th International …, 2016 – opus.lib.uts.edu.au
… Applications Tim Baldwin Maria Liakata Dialog Processing and Dialog Systems, Multimodal Interfaces Nina Dethlefs Simon Keizer Giuseppe Riccardi Speech Recognition, Text-To-Speech, Spoken Language Understanding Florian Metze Chung-Hsien Wu …

Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Y Matsumoto, R Prasad – Proceedings of COLING 2016, the 26th …, 2016 – aclweb.org
… Applications Tim Baldwin Maria Liakata Dialog Processing and Dialog Systems, Multimodal Interfaces Nina Dethlefs Simon Keizer Giuseppe Riccardi Speech Recognition, Text-To-Speech, Spoken Language Understanding Florian Metze Chung-Hsien Wu …

A learned representation for artistic style
V Dumoulin, J Shlens, M Kudlur, A Behboodi… – arXiv preprint arXiv …, 2016 – arxiv.org
… arXiv:1610.07862 [pdf] Title: Intelligence in Artificial Intelligence. Authors: Shoumen Palit Austin Datta. Subjects: Artificial Intelligence (cs.AI). arXiv:1610.07882 [pdf, other] Title: Maxmin convolutional neural networks for image classification. …

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
Non-verbal communication involves encoding, transmission and decoding of non-lexical cues and is realized using vocal (eg prosody) or visual (eg gaze, body.

Understanding Satirical Articles Using Common-Sense
D Goldwasser, X Zhang – Transactions of the Association for …, 2016 – transacl.org
Page 1. Understanding Satirical Articles Using Common-Sense Dan Goldwasser Purdue University Department of Computer Science dgoldwas@purdue.edu Xiao Zhang Purdue University Department of Computer Science zhang923@purdue.edu Abstract …

Symbol emergence in robotics: a survey
T Taniguchi, T Nagai, T Nakamura, N Iwahashi… – Advanced …, 2016 – Taylor & Francis
Page 1. ADVANCED ROBOTICS, 2016 VOL. 30, NOS. 11–12, 706–728 http://dx.doi.org/10.1080/01691864.2016.1164622 SURVEY PAPER Symbol emergence in robotics: a survey Tadahiro Taniguchia, Takayuki Nagaib, Tomoaki …

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
… Convolutional neural networks were used in [7] to automatically extract features for an MLP classifier for mispronunciation error detection … Heldner, “An instantaneous vector representation of delta pitch for speaker-change prediction in conversation dialogue system,” in ICASSP …

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.

Overview of NTCIR-12.
K Kishida, MP Kato – NTCIR, 2016 – research.nii.ac.jp
… trials. LEMoRe integrates classical image descriptors with high-level semantic concepts extracted by Convolutional Neural Networks (CNN), powered by a graphic user interface that uses natural language processing. Although …

Semi-Supervised Learning with Sparse Autoencoders in Automatic Speech Recognition
AK Dhaka – 2016 – diva-portal.org
… In dialogue systems, this tex- tual representation is fed to a language understanding module that extracts the semantic information to be … In some very recent stud- ies [1], Convolutional Neural Networks have been applied to the speech samples directly, eliminating the need for …

Challenges on Multimedia for Decision-Making in the Era of Cognitive Computing
MF Moreno, R Brandão… – Multimedia (ISM), 2016 …, 2016 – ieeexplore.ieee.org
… Current research in NLP is mostly geared towards designing dialog systems capable of interacting naturally with people. … Deep learning can be described as a set of learning techniques that employs convolutional neural network training. …

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
… The representations are extracted automatically by apply- ing different filters. The most used Deep Learning architec- ture in vision is the CNN (Convolutional Neural Network) which consists of several layers. In each layer, a certain level of feature is extracted. …

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. …

How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature
A Joshi, V Tripathi, P Bhattacharyya… – Proceedings of the …, 2016 – 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. …

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. …

Personalised Dialogue Management for Users with Speech Disorders
I Casanueva – 2016 – etheses.whiterose.ac.uk
… Observable Markov Decision Process (POMDP) based Dialogue Management (DM) has been shown to improve the interaction perfor- mance in challenging ASR environments, but most of the research in this area has focused on Spoken Dialogue Systems (SDSs) developed …

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. They compare their approach against recursive SVM, and show an improvement in case of deep learning architecture. …

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.

Affective analysis and Modeling of Spoken Dialogue Transcripts
E Palogiannidi – 2016 – researchgate.net
… Jose Lopes, Arodami Chorianopoulou, Elisavet Palogiannidi, Helena Moniz, Alberto Abad, Katerina Louka, Elias Iosif and Aleandros Potamianos “The SpeDial Datasets: Datasets for Spoken Dialogue Systems Analytics”, in Proceedings of the 10th edition of the Language …

On bridging the semantic gap in knowledge-based question answering
P Yin? ??? – HKU Theses Online (HKUTO), 2016 – hub.hku.hk
Page 1. Title On bridging the semantic gap in knowledge-based question answering Author(s) Yin, Pengcheng; ??? Citation Yin, P. [???]. (2016). On bridging the semantic gap in knowledge-based question answering. (Thesis). …

Energy-scalable speech recognition circuits
M Price – 2016 – dspace.mit.edu
… BC best-case CDF cumulative distribution function CMOS complementary metal-oxide semiconductor CMVN cepstral mean and variance normalization CNN convolutional neural network DAC … latency. In dialogue systems, the …

Computer Vision and Natural Language Processing: Recent Approaches in Multimedia and Robotics
P Wiriyathammabhum, D Summers-Stay… – ACM Computing …, 2016 – dl.acm.org
… in which attributes are augmented to nodes in the parse tree. 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. …

Language-independent methods for computer-assisted pronunciation training
A Lee – 2016 – dspace.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) . . . . …

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 parameterization, developments in information security for automated speech, forensic speaker recognition, use …

Mobile Manipulation, Tool Use, and Intuitive Interaction for Cognitive Service Robot Cosero
J Stückler, M Schwarz… – Frontiers in Robotics …, 2016 – pdfs.semanticscholar.org
… shown in Figure 7A bottom left. Both RGB images are presented to a convolutional neural network, which has been pretrained on the ImageNet data set for categorization of natural images. This produces semantic higher-layer …

From predictive to interactive multimodal language learning
A Lazaridou – 2016 – eprints-phd.biblio.unitn.it
… autoencoders. [49] adopt instead a simple concatenation strategy, but obtain empir- ical improvements by using state-of-the-art convolutional neural networks to extract 26 Page 27. … together with the pre-trained convolutional neural network of [54]. The vector corre- …

Machine Learning: The New AI
E Alpaydin – 2016 – books.google.com
Page 1. MACHINE LEARNING ETHEM ALPAYDIN O 1 000 1 0 1 0 1 1 1 0 1 000 1 1 0 1 000 O 1 1 00 1 0 1 0 1 1 0 1 1 0 1 00 1 00000 O 1 00000 1 0 1 1 0 1 1 000 1 1 1 0000 O 1 1 0000 1 0 1 1 1 1 00 1 0 1 1 00 1 00 O 1 1 0 …

Exploiting Semantic and Topic Context to Improve Recognition of Proper Names in Diachronic Audio Documents
I Sheikh – 2016 – tel.archives-ouvertes.fr
Page 1. Exploiting Semantic and Topic Context to Improve Recognition of Proper Names in Diachronic Audio Documents Imran Sheikh To cite this version: Imran Sheikh. Exploiting Semantic and Topic Context to Improve Recognition …

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 …

Prosody Utilization in Continuous Speech Recognition
J Bartošek – 2016 – dspace.cvut.cz
… Czech phrase modality detection from acoustic signal is covered and together with existing phrase boundary detector can such system serve as an punctuation module for Czech dictation ASR system or in Czech dialogue system to support its natural language processing (NLP …

Social Sensorimotor Contingencies
J Bütepage – 2016 – diva-portal.org
… Incoming charge from other neurons is accumulated. If this charge reaches a certain threshold, an action potential is generated and the neuron fires, ie it sends a signal to other neurons. c) A convolutional neural network adopted from [50]. …

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. …

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 …

Large-scale affective computing for visual multimedia
B Jou – 2016 – search.proquest.com
… CNN convolutional neural network. … These methods for facial expression recognition span a broad spectrum from Gabor wavelets [Lyons et al., 1998] to local binary patterns (LBP) [Shan et al., 2009], and more recently, to convolutional neural networks (CNNs) [Tang, 2013]. …

An Investigation into Language Model Data Augmentation for Low-Resourced STT and KWS}}
G Huang, TF da Silva, L Lamel, JL Gauvain, A Gorin… – ieeeicassp, 2016 – perso.limsi.fr
LIMSI TLP group publication list starting from 1990. An URL is given for each reference having a PostScript file and an abstract in the Online publication list. @STRING{arpaslt = “Proceedings of ARPA Workshop on Spoken Language …