MaxEnt & Dialog Systems 2017


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100 Best Apache OpenNLP Videos | Apache OpenNLP & Dialog Systems


Two-stage multi-intent detection for spoken language understanding
B Kim, S Ryu, GG Lee – Multimedia Tools and Applications, 2017 – Springer
… All the models that are needed to process the method are constructed from only the text corpus that is to train the dialog system. 3.1 ASR error detection … To train SID from SI-labeled training data, we used maximum entropy (MaxEnt) classifier [13] …

A simple generative model of incremental reference resolution for situated dialogue
C Kennington, D Schlangen – Computer Speech & Language, 2017 – Elsevier
… early as possible (in other words, time is shared). The overall goal of this paper is to model L’s comprehension process and implement it as a component in a spoken dialogue system. More formally, this can be modelled as a …

Domain Complexity and Policy Learning in Task-oriented Dialogue Systems
A Papangelis, S Ultes, Y Stylianou – uni-ulm.de
… Page 5. Domain Complexity and Policy Learning in Task-oriented Dialogue Systems 5 Mdl1 Mdl2 Mdl3 Mdl4 Mdl5 Mdl6 Mdl7 Mdl8 … Coverage MaxNEnt SumNEnt StdVal StdEnt MaxNEnt MaxNEnt MaxEnt 0.382 -0.194 -0.457 1.440 -0.547 0.268 0.313 -0.189 …

Dialogue Act Segmentation for Vietnamese Human-Human Conversational Texts
TL Ngo, KL Pham, MS Cao, SB Pham… – arXiv preprint arXiv …, 2017 – arxiv.org
… It is important for many applications: dialogue systems, automatic translation machine [2], automatic speech recognition, etc [3] [4] and has been studied in various languages such as English, Chinese, Arabic, Czech … A. Maximum Entropy The ME (Maxent) model defines …

Eckhard Bick and Marcos Zampieri
J Koco?, M Marci?czuk, A Aghaebrahimian, F Jur?í?ek… – pdfs.semanticscholar.org
… 37 Elena Tutubalina Digging Language Model – Maximum Entropy Phrase Extraction. . . . . 46 Jakub Kanis … 470 Meysam Asgari, Allison Sliter, and Jan Van Santen Platon: Dialog Management and Rapid Prototyping for Multilingual Multi-user Dialog Systems …

Using Context Information for Dialog Act Classification in DNN Framework
Y Liu, K Han, Z Tan, Y Lei – Proceedings of the 2017 Conference on …, 2017 – aclweb.org
… work on dialog act (DA) classifi- cation has investigated different methods, such as hidden Markov models, maximum entropy, conditional random … man conversations, as well as for developing intel- ligent human-to-computer dialog systems (either written or spoken dialogs) …

On the Identification of Suggestion Intents from Vietnamese Conversational Texts
TL Ngo, KL Pham, H Takeda, SB Pham… – Proceedings of the Eighth …, 2017 – dl.acm.org
… We investigate two approaches to filter functional segment containing suggestion intents: (1) machine learning approach with maximum entropy (Maxent) model and (2 … from this participant, more so than from the other participants”[8]. In detail, in dialog systems, an addressee is …

Chatbots for troubleshooting: A survey
C Thorne – Language and Linguistics Compass, 2017 – Wiley Online Library
… 3.1.3 NLP resources. Last, but not least, to support NLU and NLG, text-based dialog systems and chatbots alike tend to leverage NLP techniques and resources to understand user input and generate a response … (2007), No, Classifier (maximum entropy), Knowledge base, Mixed …

Statistical Language and Speech Processing: 5th International Conference, SLSP 2017, Le Mans, France, October 23–25, 2017, Proceedings
N Camelin, Y Estève, C Martín-Vide – 2017 – books.google.com
… Speech and language generation Speech recognition Speech synthesis Speech transcription Spelling correction Spoken dialogue systems Term extraction Text … 143 Manny Rayner, Nikos Tsourakis, and Johanna Gerlach Low Latency MaxEnt-and RNN-Based Word Sequence …

Natural language processing
K Sirts – 2017 – courses.cs.ut.ee
… Natural language generation • Text summarization • Dialog systems 23 Page 24. The general plan • A matrix of tasks and methods Tasks Classical Feature-?based Neural networks Language modeling Ngram model Maximum entropy model Recurrent neural networks Parsing …

Just ASK: Building an Architecture for Extensible Self-Service Spoken Language Understanding
A Kumar, A Gupta, J Chan, S Tucker… – arXiv preprint arXiv …, 2017 – arxiv.org
… ASK allows AI researchers to experiment on conversation and dialog systems without the additional overhead of maintaining their own ASR and NLU … As the data in sample utterances is often imbalanced, we follow the principle of maximum entropy to impose uniform pri- ors on …

Anjishnu Kumar Amazon. com anjikum@ amazon. com
S Tucker, B Hoffmeister, M Dreyer, S Peshterliev… – alborz-geramifard.com
… ASK allows researchers to experiment on conversation and dialogue systems without the additional overhead of maintaining their own ASR and NLU … As the data in sample utterances is often imbalanced, we follow the principle of maximum entropy to impose uniform pri- ors …

AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
FL Li, M Qiu, H Chen, X Wang, X Gao, J Huang… – Proceedings of the …, 2017 – dl.acm.org
… Both models are much better than our baseline one (an ensemble of SVM and MaxEnt, 82.71%) … 2016. Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models. In AAAI’16. 3776–3784 …

Grounding language by continuous observation of instruction following
T Han, D Schlangen – Proceedings of the 15th Conference of the …, 2017 – aclweb.org
… Grounding Language by Continuous Observation of Instruction Following Ting Han and David Schlangen CITEC, Dialogue Systems Group, Bielefeld University first.last@uni-bielefeld.de Abstract … U = {at1 , …. ati } (2) 3 Modeling the learning task 3.1 Maximum entropy model …

Combining speech-based and linguistic classifiers to recognize emotion in user spoken utterances
D Griol, JM Molina, Z Callejas – Neurocomputing, 2017 – Elsevier
… Although emotion is receiving increasing attention from the dialog systems community, most research described in the literature is … of the classification function include Naive Bayes (NB), neural networks (NN), Probabilistic Classifiers (PC), Maximum Entropy (ME), Stochastic …

Inverse Reinforcement Learning based Human Behavior Modeling for Goal Recognition in Dynamic Local Network Interdiction
Y Zeng, K Xu, Q Yin, L Qin, Y Zha, W Yeoh – 2017 – planrec.org
… Then maximum entropy IRL learns the op- ponent behavior model through weight estimation of pre- defined behavior features, through calculating the opponent feature count fevader, the number of times features were ob- served in the set of trajectories …

A hierarchical neural model for learning sequences of dialogue acts
QH Tran, I Zukerman, G Haffari – Proceedings of the 15th Conference of …, 2017 – aclweb.org
… This task is particularly useful for dialogue systems, as knowing the DA of an utterance sup- ports its interpretation, and the generation of an … our model conditions on the full his- tory, rather than a finite history as done in Markov models, such as maximum entropy Markov mod …

Statistical Language and Speech Processing
N Camelin, Y Estève, C Martín-Vide – Springer
… Speech and language generation Speech recognition Speech synthesis Speech transcription Spelling correction Spoken dialogue systems Term extraction Text … 143 Manny Rayner, Nikos Tsourakis, and Johanna Gerlach Low Latency MaxEnt-and RNN-Based Word Sequence …

Year of Publication: 2016
AM Pundge, SA Khillare, CN Mahender – pdfs.semanticscholar.org
… Bobrow DG, Kaplan RM, Kay M, Norman DA, Thompson H, and Winograd T. Gus, a frame-driven dialog system … Deepak Ravichandran, Abraham Ittycheriah and salim roukos, “Automatic Derivation of surface text pattern for a maximum Entropy Based question answering system …

How Online Emotions Influence Community Life
J Sienkiewicz, A Chmiel, P Sobkowicz, JA Ho?yst – Cyberemotions, 2017 – Springer
… The tendency for the isolated system to increase its entropy and to evolve to reach the state characterized by the maximum entropy (MaxEnt) is a well-know physical phenomenon previously observed in many real-world systems (Harte 2011.

Zero-shot learning across heterogeneous overlapping domains
A Kumar, PR Muddireddy, M Dreyer… – Proc. Interspeech, 2017 – isca-speech.org
… Natural Language Understanding (NLU) in many commercial spoken dialog systems uses a simplified shallow semantic pars- ing formalism which attempts to … a probability dis- tribution over the training classes Ytrain using a softmax layer similar to a maximum entropy model …

Foreword to the Special Issue on Uralic Languages
TA Pirinen, HZ für Sprachkorpora, T Trosterud… – 2017 – nejlt.ep.liu.se
… HunTag8, a MaxEnt-based sequential tagger can be used as a shallow parser [13] or as a Named Entity recogniser [14] as well … Constraint grammar in dialogue systems. In NEALT Proceedings Series, volume 8, pages 31–21, 2009 …

Evaluating LSTM Networks, HMM and WFST in Malay Part-of-Speech Tagging
TP Tan, B Ranaivo-Malançon… – Journal of …, 2017 – journal.utem.edu.my
… in modeling sequential data such as phoneme recognition, speech translation, language modeling, speech synthesis, chatbot-like dialog systems and others … learning techniques have been tested such as decision trees [11], k-nearest neighbor [11], maximum entropy model [12 …

AppTechMiner: Mining Applications and Techniques from Scientific Articles
M Singh, S Dan, S Agarwal, P Goyal… – Proceedings of the 6th …, 2017 – dl.acm.org
… Information Extraction, Chinese Word Segmenta- tion, Semantic Role Labeling, Information Retrieval, Entity Recog- nition, Word Alignment, Conditional Random Fields, Maximum Entropy, Coreference Resolution, Machine Learning, Dialogue Systems, Textual Entailment …

Disfluency Detection using a Noisy Channel Model and a Deep Neural Language Model
PJ Lou, M Johnson – Proceedings of the 55th Annual Meeting of the …, 2017 – aclweb.org
… Moreover, disfluen- cies pose a major challenge to natural language processing tasks, such as dialogue systems, that rely on speech transcripts (Ostendorf et al., 2008) … The language model scores are used as features in a MaxEnt reranker to select the most plausible analysis …

The technology behind personal digital assistants: an overview of the system architecture and key components
R Sarikaya – IEEE Signal Processing Magazine, 2017 – ieeexplore.ieee.org
… user’s recent relevant activity (eg, similar content searches), which are captured in the history variable h in (1). This is used to model whether a specific user u will like the specific sug- gested entity .e Standard machine-learning techniques, such as maximum entropy models [35 …

A Two-stage Sieve Approach for Quote Attribution
G Muzny, M Fang, A Chang, D Jurafsky – … of the 15th Conference of the …, 2017 – aclweb.org
… Quote attribution, determining who said what in a given text, is important for tasks like creating dialogue systems, and in newer areas like computational … We train a binary classifier, using a maxent model to distinguish between the correct and incorrect can- didate mentions …

Unbounded cache model for online language modeling with open vocabulary
E Grave, MM Cisse, A Joulin – Advances in Neural Information …, 2017 – papers.nips.cc
… [48] R. Rosenfeld. A maximum entropy approach to adaptive statistical language modeling … PAMI, 2013. [50] IV Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI, 2016 …

Active Learning for Visual Question Answering: An Empirical Study
X Lin, D Parikh – arXiv preprint arXiv:1711.01732, 2017 – arxiv.org
… This strategy selects unlabeled examples whose label the model is most uncertain about (maximum entropy). Curiosity-driven learning – maximizing information gain in model space … It selects (Q, I) whose answer A’s distribution has maximum entropy …

Remembering a Conversation–A Conversational Memory Architecture for Embodied Conversational Agents
M Elvir, AJ Gonzalez, C Walls, B Wilder – Journal of Intelligent …, 2017 – degruyter.com
Jump to ContentJump to Main Navigation …

CCG Supertagging via Bidirectional LSTM-CRF Neural Architecture
R Kadari, Y Zhang, W Zhang, T Liu – Neurocomputing, 2017 – Elsevier
… In literature, dominant approaches that have a rich history of NLP-related applications exploit machine learning algorithms in order to solve this problem such as Maximum Entropy [3] and Neural Networks (NN) with Conditional Random Fields (CRFs) [4]. Machine learning …

Adapting to a listener with incomplete lexical semantics
S Srinivas, B Landau, C Wilson – pdfs.semanticscholar.org
… Note that the model assumes independence of attributes, an idealization that we show to be largely effective but which is not inherent to the maximum entropy formalism … Adaptive referring expression generation in spoken dialogue systems: Evaluation with real users …

Overview of dialogue breakdown detection challenge 3
R Higashinaka, K Funakoshi, M Inaba… – … of Dialog System …, 2017 – workshop.colips.org
… dedicated to dialogue breakdown detection [1]. The aim of the challenge was to bring together various methods for detecting dialogue breakdown so that the capability of chat- oriented dialogue systems can be improved … SAM2017 Soochow University 1 Maximum entropy model …

Question Answering System: A Review On Question Analysis, Document Processing, And Answer Extraction Techniques.
FS UTOMO, N SURYANA… – Journal of Theoretical & …, 2017 – search.ebscohost.com
… Techniques Output [84] Question classification (using Maximum Entropy and applied n-grams) Query expansion with named entity … [51] Tokenizing, POS tagging, Name Entity Recognition (NER), question classification using Maximum Entropy Classifier …

Modelling Human Decision-making based on Aggregate Observation Data
A Kangasrääsiö, S Kaski – users.ics.aalto.fi
… 63–71. 2004. doi: 10.1007/978-3-540-28650-9 4. Ziebart, Brian D., Maas, Andrew L., Bagnell, J. Andrew, and Dey, Anind K. Maximum entropy inverse reinforce- ment learning. In Proceedings of the National Confer- ence on Artificial Intelligence, pp. 1433–1438, 2008 …

Web Application for Romanian Language Phonetic Transcription
J Domokos, ZA Szakács – MACRo 2015, 2017 – degruyter.com
… Bermuda performs phonetic transcription for out-of-vocabulary words using a Maximum Entropy classifier and a custom designed algorithm … A. Buzo, CS Petrea, D. Ghelmez-Hane, “Spontaneous speech recognition for Romanian in spoken dialogue systems” in Proceedings of …

Sentiments Analysis of Reviews Based on ARCNN Model
X Xu, M Xu, J Xu, N Zheng, T Yang – IOP Conference Series …, 2017 – iopscience.iop.org
… The performance of machine learning methods such as Support Vector Machine [5], Naïve Bayes, Maximum Entropy model, Random Walk … Building end-to-end dialogue systems using generative hierarchical neural network models[C]. Thirtieth AAAI Conference on Artificial …

Detecting sarcasm in customer tweets: an NLP based approach
S Mukherjee, PK Bala – Industrial Management & Data Systems, 2017 – emeraldinsight.com
… of detecting sarcasm has been recognized in many computer interaction-based applications, such as review summarization, dialogue systems and review … We did supervised learning, using Naïve Bayes and maximum entropy classifier to differentiate between a sarcastic and a …

Sequence-to-sequence models for punctuated transcription combining lexical and acoustic features
O Klejch, P Bell, S Renals – Acoustics, Speech and Signal …, 2017 – ieeexplore.ieee.org
… [3] I. V Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau, “Building end-to-end dialogue systems using generative hier- archical neural network models,” in AAAI, 2016. 5703 Page 5 … [11] J. Huang and G. Zweig, “Maximum entropy model for punc- tuation annotation from …

End-to-end joint learning of natural language understanding and dialogue manager
X Yang, YN Chen, D Hakkani-Tür… – … , Speech and Signal …, 2017 – ieeexplore.ieee.org
… Index Terms— language understanding, spoken dialogue systems, end-to-end, dialogue manager, deep learning … methods, such as hidden Markov models (HMMs) and conditional random field (CRF) are widely used in slot tagging tasks [4–6]; maximum entropy and support …

Inverse Reinforcement Learning from Summary Data
A Kangasrääsiö, S Kaski – arXiv preprint arXiv:1703.09700, 2017 – arxiv.org
… driver route modelling (Ziebart et al., 2008), helicopter acrobat- ics (Abbeel et al., 2010), learning to perform motor tasks (Boularias et al., 2011), dialogue systems (Chandramo- han et … A characteristic example of this is the Maximum Entropy IRL method proposed by Ziebart et al …

Multi-level Knowledge Processing in Cognitive Technical Systems
T Geier, S Biundo – Companion Technology, 2017 – Springer
… It is thus not very surprising that dialogue systems [49, 50] and human-computer interaction [30] are one of the applications of … There exist numerous flavours of graphical models for the representation of time series data, such as maximum entropy Markov models [26], conditional …

Improving the understanding of spoken referring expressions through syntactic-semantic and contextual-phonetic error-correction
I Zukerman, A Partovi – Computer Speech & Language, 2017 – Elsevier
… system. Abstract. Despite recent advances in automatic speech recognition, one of the main stumbling blocks to the widespread adoption of Spoken Dialogue Systems is the lack of reliability of automatic speech recognizers …

Node Importance Ranking of Complex Networks with Entropy Variation
X Ai – Entropy, 2017 – mdpi.com
… Article in Special Issue Complex Dynamics of an SIR Epidemic Model with Nonlinear Saturate Incidence and Recovery Rate. Previous Article in Journal Quantum Probabilities and Maximum Entropy. Previous Article in Special …

Slim Embedding Layers for Recurrent Neural Language Models
Z Li, R Kulhanek, S Wang, Y Zhao, S Wu – arXiv preprint arXiv:1711.09873, 2017 – arxiv.org
… 2013) 67.6 1.76 4-layer IRNN-512 (Le, Jaitly, and Hinton 2015) 69.4 RNN-2048 + BlackOut sampling (Ji et al. 2015) 68.3 RNN-1024 + MaxEnt 9-gram (Chelba et al. 2013) 51.3 20 LSTM-2048-512 (Grave et al. 2016) 43.7 0.83 LightRNN (Li et al. 2016) 66.0 0.041 …

Introduction to Cyberemotions
JA Ho?yst – Cyberemotions, 2017 – Springer
… During the initial training phase special machine learning algorithms such as Naive Bayes, Maximum Entropy or Support Vector Machines try to … Outputs of the Project were used for creating new affective dialog systems as interactive tools as well as semi-automated simulation …

ExoTime: Temporal Information Extraction from Korean Texts Using Knowledge Base
YS Jeong, CG Lim, HJ Choi – ????????????, 2017 – dbpia.co.kr
… Based on the generated raw features, a set of rules is defined as the rule-based approach, and a set of features are defined to train the machine-learning models (eg, Maximum Entropy Model, Support Vector Machine, Conditional Random Fields, and Logistic Regression) …

A Complete Bibliography of ACM Transactions on Asian Language Information Processing
NHF Beebe – 2017 – tug.ctan.org
… maximum [48, 106]. maximum-entropy [48]. ME [79] … [37] Harksoo Kim and Jungyun Seo. Resolution of referring expressions in a Korean multimodal dialogue system. ACM Transactions on Asian Lan- guage Information Processing, 2(4):324–337, December 2003 …

Towards Building a Shallow Parsing Pipeline for English-Telugu Code Mixed Social Media Data
K Nelakuditi – 2017 – web2py.iiit.ac.in
… During the same time, in 1997, Ratnaparkhi [36] suggested a Maximum Entropy Frame work for Natural Language Ambiguity and applied it to POS tagging. Conditional Random Fields have been used by Feisha and Fern for shallow parsing. [38] 11 Page 22. Chapter 3 …

Natural Language Processing: State of The Art, Current Trends and Challenges
D Khurana, A Koli, K Khatter, S Singh – arXiv preprint arXiv:1708.05148, 2017 – arxiv.org
… Androutsopoulos et al.,2000b ;Rennie .,2000)[46][47][48],Memory based Learning (Androutsopoulos et al.,2000b)[47], Support vector machines (Druker et al., 1999)[49], Decision Trees (Carreras and Marquez , 2001)[50] Maximum Entropy Model (Berger et … 6.6 Dialogue System …

The MSIIP System for Dialog State Tracking Challenge 4
M Li, J Wu – Dialogues with Social Robots, 2017 – Springer
… To build a robust dialog system, it needs to maintain a distribution over multiple hypotheses of the true dialog state, which is … are investigated [2, 3, 4, 5]. Recently, discriminative models have been found that yield better performance, such as Maximum Entropy [6], Conditional …

Deep Learning for Dialogue Systems
YN Chen, A Celikyilmaz, D Hakkani-Tür – Proceedings of ACL 2017 …, 2017 – aclweb.org
… Ba- sic components of a dialog system are automatic … Understanding Traditionally, do- main identification and intent prediction are framed as utterance classification problems, where several classifiers such as support vector ma- chines and maximum entropy have been em …

A knowledge and reasoning toolkit for cognitive applications
M Canim, C Cornelio, R Farrell, A Fokoue… – Proceedings of the fifth …, 2017 – dl.acm.org
… Supervised methods include maximum entropy models [31] [43] and conditional random fields [51] … Ultimately, a question answering system or dialogue system should be able to query the resulting graph to answer ques- tions involving temporal and spatial reasoning over …

Using Knowledge Graph And Search Query Click Logs in Statistical Language Model For Speech Recognition
W Zhu – Proc. Interspeech 2017, 2017 – isca-speech.org
… R. Lau, R. Rosenfeld, and S. Roukos, “Trigger-based language models: A maximum entropy approach,” in ICASSP, IEEE, vol. 2. IEEE, 1993, pp. 45–48. [7] R. Sarikaya, Y. Gao, H. Erdogan, and M. Picheny, “Turn-based language modeling for spoken dialog systems,” in ICASSP …

Robust dialog state tracking for large ontologies
F Dernoncourt, JY Lee, TH Bui, HH Bui – Dialogues with Social Robots, 2017 – Springer
… Yet, dialog state tracking is crucial for reliable operations of a spoken dialog system because the latter relies on the estimated dialog … approaches in the previous DSTCs include neural networks [1, 2, 3], web-style ranking and SLU combination [4], maximum entropy models [5 …

Emotion Recognition: A Literature Survey
S Goyal, N Tiwari – International Journal For Technological Research In … – ijtre.com
… Applications: automatic answering systems, dialogue systems, and human like robots … for movie reviews as a dataset to indicate negative and positive statements and well suited for other machine learning approaches like Support Vector Machine, Maximum Entropy and Naive …

Automatic assessment of depression based on visual cues: A systematic review
A Pampouchidou, P Simos, K Marias… – IEEE Transactions …, 2017 – ieeexplore.ieee.org
Page 1. 1949-3045 (c) 2017 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org …

Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection
T Kato, T Shinozaki – arXiv preprint arXiv:1711.03689, 2017 – arxiv.org
… 2007–2011. [15] F. Wang, “A multi-agent reinforcement learning algorithm for disambiguation in a spoken dialogue system,” in Proceedings of the 2010 … 81–85. [17] C. Molina, NB Yoma, F. Huenupan, C. Garreton, and J. Wuth, “Maximum entropy-based reinforcement learning us …

Toward Human Parity in Conversational Speech Recognition
W Xiong, J Droppo, X Huang, F Seide… – … on Audio, Speech …, 2017 – ieeexplore.ieee.org
… N-grams. All N-gram LMs were estimated by a maximum entropy criterion as described in [67]. The … News texts. The N-gram LM configuration is modeled after that described in [61], except that maxent smoothing was used. The …

Autonomous social gaze model for an interactive virtual character in real-life settings
Z Yumak, B Brink, A Egges – Computer Animation and Virtual …, 2017 – Wiley Online Library
… labeling. They applied a maximum entropy model to detect engagement combining several features such as location of the face, width and height, confidence score of the face, trajectory of location features, and attention. They …

Neural machine translation and sequence-to-sequence models: A tutorial
G Neubig – arXiv preprint arXiv:1703.01619, 2017 – arxiv.org
… This is because models in this chapter can be motivated in two ways: log-linear models that calculate un-normalized log-probability scores for each function and normalize them to probabilities, and maximum-entropy models that spread their probability mass as evenly as …

Subjective Text Mining for Arabic Social Media
NFB Hathlian, AM Hafez – … Journal on Semantic Web and Information …, 2017 – igi-global.com
… Those approaches differ in the algorithm (eg, NB Classifier, SVM, Maximum Entropy, Page 4 … Hijjawi, M., & Bander, Z. (2011). An Arabic Stemming Approach using Machine Learning with Arabic Dialogue System. Proceedings of the ICGST AIML ’11Conference. Dubai: UAE …

Maximum-likelihood augmented discrete generative adversarial networks
T Che, Y Li, R Zhang, RD Hjelm, W Li, Y Song… – arXiv preprint arXiv …, 2017 – arxiv.org
… with a moving reward signal monotone in D(x). Define the normalized probability distribution q (x) = 1 Z(D) D(x)1/? in some bounded region to guarantee integrability (note that D is an approximation to pd p+pd if D is well trained) and also put a maximum-entropy regularizer H …

Attention-based multimodal fusion for video description
C Hori, T Hori, TY Lee, K Sumi, JR Hershey… – arXiv …, 2017 – openaccess.thecvf.com
Page 1. Attention-Based Multimodal Fusion for Video Description Chiori Hori Takaaki Hori Teng-Yok Lee Ziming Zhang Bret Harsham John R. Hershey Tim K. Marks Kazuhiko Sumi? Mitsubishi Electric Research Laboratories …

Combining CNNs and Pattern Matching for Question Interpretation in a Virtual Patient Dialogue System
L Jin, M White, E Jaffe, L Zimmerman… – Proceedings of the 12th …, 2017 – aclweb.org
… This work utilizes word- and character-based convolutional neural net- works (CNNs) for question identification in a virtual patient dialogue system, out- performing a strong word- and character- based logistic regression … (2011) observed maximum entropy systems performed …

Be More Eloquent, Professor ELIZA–Comparison of Utterance Generation Methods for Artificial Second Language Tutor
T Nakamura, R Rzepka, K Araki, K Inui – pdfs.semanticscholar.org
… utterances. We compared the utterances generated by our methods with those of other dialogue systems … 1.1 Traditional vs. Web-based Dialogue Systems Well-known chatbots are ELIZA [Weizenbaum, 1966] and ALICEBOT1. ELIZA …

Boosting a Rule-Based Chatbot Using Statistics and User Satisfaction Ratings
O Efraim, V Maraev, J Rodrigues – Conference on Artificial Intelligence …, 2017 – Springer
… frameworks that have been proposed over time to assess the subjective satisfaction or acceptance of users of dialogue systems, chiefly spoken … MElt is a maximum-entropy Markov-model POS tagger and lemmatiser with a normaliser/corrector wrapper trained on user-generated …

Deep Energy-Based Models for Structured Prediction
D Belanger – 2017 – scholarworks.umass.edu
… system used as the interface between a computer and a user. For example, when a dialogue system responds to a user query, it may produce its response as a sentence containing multiple words, and this sentence may be further converted into an audio …

Conversational bootstrapping and other tricks of a concierge robot
S Guo, J Lenchner, J Connell, M Dholakia… – Proceedings of the 2017 …, 2017 – dl.acm.org
… 12, 13, 25, 32], maximum entropy models [25, 28], and others. In addition, there are hybrid approaches that combine rule-based and learning based approaches [13, 21, 32]. Apart from text classification, the bulk of the previous work on learning in dialog systems has to do with …

Natural Language Processing for Social Media
A Farzindar, D Inkpen – Synthesis Lectures on Human …, 2017 – 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 …

Automatic sentence stress feedback for non-native English learners
GG Lee, HY Lee, J Song, B Kim, S Kang, J Lee… – Computer Speech & …, 2017 – Elsevier
Skip to main content …

Sequential short-text classification with neural networks
F Dernoncourt – 2017 – dspace.mit.edu
… tial short-text classification are based on non-ANN approaches, such as Hidden Markov Models (HMMs) [93, 106], maximum entropy [3], naive Bayes [75], and CRF … Models (HMMs) [93, 106, 108], maximum entropy [3], naive Bayes [75], and conditional …

Automatic chinese factual question generation
M Liu, V Rus, L Liu – IEEE Transactions on Learning …, 2017 – ieeexplore.ieee.org
… Question authoring tools are important in educational technologies, eg, intelligent tutoring systems, as well as in dialogue systems … Professor He et al. [23] from TianJing University first trained a maximum entropy classifier to identify the semantic chunk of a noun phrase …

Novel Methods for Natural Language Generation in Spoken Dialogue Systems
O Dušek – 2017 – dspace.cuni.cz
… Ond?ej Dušek Novel Methods for Natural Language Generation in Spoken Dialogue Systems Institute of Formal and Applied Linguistics Supervisor: Ing … iii Page 4. Page 5. Title: Novel Methods for Natural Language Generation in Spoken Dialogue Systems Author: Ond?ej Dušek …

Virtual debate coach design: assessing multimodal argumentation performance
V Petukhova, T Mayer, A Malchanau… – Proceedings of the 19th …, 2017 – dl.acm.org
Page 1. Virtual Debate Coach Design: Assessing Multimodal Argumentation Performance Volha Petukhova Spoken Language Systems Group, Saarland University Saarbrücken, Germany v.petukhova@lsv.uni-saarland.de Tobias …

Constructing a Natural Language Inference dataset using generative neural networks
J Starc, D Mladeni? – Computer Speech & Language, 2017 – Elsevier
… Some generative models are built to generate a single optimal response given the input. Such models have been applied to machine translation (Sutskever et al., 2014), image caption generation(Xu et al., 2015), or dialog systems (Serban et al., 2016a) …

A Survey on Dialogue Systems: Recent Advances and New Frontiers
H Chen, X Liu, D Yin, J Tang – arXiv preprint arXiv:1711.01731, 2017 – arxiv.org
… A statistical dialog system maintains a distribution over multiple hypotheses of the true dialog state, facing with noisy conditions and … riety of statistical approaches, including robust sets of hand- crafted rules [80], conditional random fields [28; 27; 53], maximum entropy models [85 …

End-to-End Online Speech Recognition with Recurrent Neural Networks
K Hwang – 2017 – s-space.snu.ac.kr
… more important than the latency. On the other hand, the online ASR, or incremental speech recognition (ISR) [12], is more focused on the decoding latency, and usually employed for real-time applications such as spoken dialog systems or real-time auto- matic captioning …

Learning an Executable Neural Semantic Parser
J Cheng, S Reddy, V Saraswat, M Lapata – arXiv preprint arXiv …, 2017 – arxiv.org
… For example, Wong and Mooney (2006) use word alignment as the basis of extracting a synchronous grammar whose rules are subsequently scored with maximum-entropy model, whereas Andreas, Vlachos, and Clark (2013) use a phrase-based translation model, where …

KIT-Conferences
MIAR Roedder – 2017 – isl.anthropomatik.kit.edu
… 04, 2017. Yeah, Right, Uh-Huh: A Deep Learning Backchannel Predictor, Robin Ruede, Markus Müller, Sebastian Stüker, Alex Waibel. International Workshop on Spoken Dialogue Systems Technology 2017, Farmington, Pennsylvania, USA. 6th – 9th June, 2017 …

Learning Algorithms for Broad-Coverage Semantic Parsing
S Swayamdipta – 2017 – cs.cmu.edu
Page 1. Learning Algorithms for Broad-Coverage Semantic Parsing Swabha Swayamdipta September 2017 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Thesis Committee: Noah A. Smith, Chair …

Multimodal Analysis of User-Generated Multimedia Content
R Shah, R Zimmermann – 2017 – Springer
… Examples of the second domain will include, but not limited to: computational and psychological models of emotions, bodily manifestations of affect (facial expressions, posture, behavior, physiology), and affective interfaces and applications (dialogue systems, games, learning …

Training end-to-end dialogue systems with the ubuntu dialogue corpus
RT Lowe, N Pow, IV Serban, L Charlin… – Dialogue & …, 2017 – dad.uni-bielefeld.de
… doi: 10.5087/dad.2017.102 Training End-to-End Dialogue Systems with the … As a result of this analysis, we suggest some promising directions for future research on the Ubuntu Dialogue Corpus, which can also be applied to end-to-end dialogue systems in general …

Natural language processing in mental health applications using non-clinical texts
RA Calvo, DN Milne, MS Hussain… – Natural Language …, 2017 – cambridge.org
Page 1. Natural Language Engineering: page 1 of 37. c Cambridge University Press 2017. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons. org …

A deep reinforcement learning chatbot
IV Serban, C Sankar, M Germain, S Zhang… – arXiv preprint arXiv …, 2017 – arxiv.org
… 1 Introduction Dialogue systems and conversational agents – including chatbots, personal assistants and voice- control interfaces – are becoming ubiquitous in modern society … 2 System Overview Early work on dialogue systems (Weizenbaum 1966, Colby 1981, Aust et al …

Dialogue Act Recognition for Conversational Agents
LE Hacquebord – 2017 – dspace.library.uu.nl
… 7 2.3 Dialogue System Architecture … Page 15. 3 Chapter 2 Background Information This chapter provides some background information on natural language processing (NLP) and dialogue systems that is necessary to understand the remaining parts of the thesis …

A study measuring the impact of shared decision making in a human-robot team
MQ Azhar, EI Sklar – The International Journal of Robotics …, 2017 – journals.sagepub.com
This paper presents the results of a user study in which the impact of sharing decision making in a human-robot team was measured. In the experiments outlined h…

Advanced data exploitation in speech analysis: An overview
Z Zhang, N Cummins, B Schuller – IEEE Signal Processing …, 2017 – ieeexplore.ieee.org
… For speech processing, crowdsourcing has been widely employed for a range of tasks, including speech data col- lection/acquisition, speech annotation, speech perception, assessment of speech synthesis, and dialog system evalua- tion [15], [26] …

Towards Natural Language Understanding using Multimodal Deep Learning
S Bos – pdfs.semanticscholar.org
Page 1. Towards Natural Language Understanding using Multimodal Deep Learning Steven Bos Delft Un iversity of T echnolog y Page 2. Page 3. Towards Natural Language Understanding using Multimodal Deep Learning THESIS …

Domain Transfer for Deep Natural Language Generation from Abstract Meaning Representations
N Dethlefs – IEEE Computational Intelligence Magazine, 2017 – ieeexplore.ieee.org
Page 1. 18 IEEE ComputatIonal IntEllIgEnCE magazInE | august 2017 1556-603x/17© 2017IEEE Digital Object Identifier 10.1109/MCI.2017.2708558 Date of publication: 19 July 2017 Abstract—Stochastic natural language generation …

Personalizing recurrent-neural-network-based language model by social network
HY Lee, BH Tseng, TH Wen, Y Tsao, HY Lee… – IEEE/ACM Transactions …, 2017 – dl.acm.org
Page 1. IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 25, NO. 3, MARCH 2017 519 Personalizing Recurrent-Neural- Network-Based Language Model by Social Network Hung-Yi …

Learning Semantic Patterns for Question Generation and Question Answering
HP Rodrigues – 2017 – pdfs.semanticscholar.org
… This approach of learning by analogy, or example-based systems [Aamodt and Plaza, 1994], has also been applied in other domains, such as in creation of dialog systems [Nio et al., 2014] or translation of unknown words [Langlais and Patry, 2007] …

Automatic Text Simplification
H Saggion – Synthesis Lectures on Human Language …, 2017 – 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 …

Handling long-term dependencies and rare words in low-resource language modelling
M Singh – 2017 – publikationen.sulb.uni-saarland.de
Page 1. Handling long-term dependencies and rare words in low-resource language modelling A dissertation submitted towards the degree of Doctor of Engneering of the Faculty of Mathematics and Computer Science of Saarland University by Mittul Singh, (M.Sc.) …

Advances in Neural Networks-ISNN 2017: 14th International Symposium, ISNN 2017, Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017 …
F Cong, A Leung, Q Wei – 2017 – books.google.com
Page 1. Fengyu Cong· Andrew Leung Qinglai Wei (Eds.) Advances in Neural Networks – ISNN 2017 14th International Symposium, ISNN 2017 Sapporo, Hakodate, and Muroran, Hokkaido, Japan, June 21–26, 2017 Proceedings, Part I 123 Page 2 …

Entity-Centric Discourse Analysis and Its Applications
X Wang – 2017 – repository.kulib.kyoto-u.ac.jp
Page 1. Title Entity-Centric Discourse Analysis and Its Applications( Dissertation_ ?? ) Author(s) Wang, Xun Citation Kyoto University (????) Issue Date 2017-11-24 URL https://dx.doi.org/10.14989/doctor.k20777 Right The …

Methods and Techniques for Clinical Text Modeling and Analytics
Y Ling – 2017 – search.proquest.com
Methods and Techniques for Clinical Text Modeling and Analytics. Abstract. This study focuses on developing and applying methods/techniques in different aspects of the system for clinical text understanding, at both corpus and document level …

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