Source: http://www.peterhenderson.co/paper_sites/dialogdatasets/references.html
Timestamp: 2019-04-26 06:38:48+00:00

Document:
A basic outline of a dialog system.
During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.
The authors gratefully acknowledge financial support by the Samsung Advanced Institute of Technology (SAIT), the Natural Sciences and Engineering Research Council of Canada (NSERC), the Canada Research Chairs, the Canadian Institute for Advanced Research (CIFAR) and Compute Canada. Early versions of the manuscript benefited greatly from the proofreading of Melanie Lyman-Abramovitch, and later versions were extensively revised by Genevieve Fried and Nicolas Angelard-Gontier. The authors also thank Nissan Pow, Michael Noseworthy, Chia-Wei Liu, Gabriel Forgues, Alessandro Sordoni, Yoshua Bengio and Aaron Courville for helpful discussions.
B. Aarts and S. A. Wallis. The diachronic corpus of present-day spoken english (DCPSE), 2006.
Robert Abbott, Brian Ecker, Pranav Anand, and Marilyn Walker. Internet argument corpus 2.0: An sql schema for dialogic social media and the corpora to go with it. In Language Resources and Evaluation Conference, LREC2016, 2016.
S. Afantenos, N. Asher, F. Benamara, A. Cadilhac, Cédric Dégremont, P. Denis, M. Guhe, S. Keizer, A. Lascarides, O. Lemon, et al. Developing a corpus of strategic conversation in the settlers of catan. In SeineDial 2012-The 16th workshop on the semantics and pragmatics of dialogue, 2012.
Y. Al-Onaizan, U. Germann, U. Hermjakob, K. Knight, P. Koehn, D. M., and K. Yamada. Translating with scarce resources. In AAAI, 2000.
D. Ameixa and L. Coheur. From subtitles to human interactions: introducing the subtle corpus. Technical report, Tech. rep., 2013.
A. H. Anderson, M. Bader, E. G. Bard, E. Boyle, G. Doherty, S. Garrod, S. Isard, J. Kowtko, J. McAllister, J. Miller, et al. The HCRC map task corpus. Language and speech, 34 (4): 351-366, 1991.
Jacob Andreas, Sara Rosenthal, and Kathleen McKeown. Annotating agreement and disagreement in threaded discussion. In LREC, pages 818-822. Citeseer, 2012.
L. E. Asri, J. He, and K. Suleman. A sequence-to-sequence model for user simulation in spoken dialogue systems. arXiv preprint arXiv:1607.00070, 2016.
A. J. Aubrey, D. Marshall, P. L. Rosin, J. Vandeventer, D. W. Cunningham, and C. Wallraven. Cardiff conversation database (CCDb): A database of natural dyadic conversations. In Computer Vision and Pattern Recognition Workshops (CVPRW), IEEE Conference on, pages 277-282, 2013.
H. Aust, M. Oerder, F. Seide, and V. Steinbiss. The philips automatic train timetable information system. Speech Communication, 17 (3): 249-262, 1995.
R. E. Banchs. Movie-DiC: a movie dialogue corpus for research and development. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers, 2012.
R. E. Banchs and H. Li. IRIS: a chat-oriented dialogue system based on the vector space model. In Proceedings of the ACL 2012 System Demonstrations, 2012.
S. Banerjee and A. Lavie. METEOR: An automatic metric for mt evaluation with improved correlation with human judgments. In Proceedings of the ACL workshop on intrinsic and extrinsic evaluation measures for machine translation and/or summarization, 2005.
M. Barlow. Corpus of spoken, professional american-english, 2000.
J. Beare and B. Scott. The spoken corpus of the survey of english dialects: language variation and oral history. In Proceedings of ALLC/ACH, 1999.
Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. The Journal of Machine Learning Research, 3: 1137-1155, 2003.
Y. Bengio, I. Goodfellow, and A. Courville. Deep learning. An MIT Press book in preparation. Draft chapters available at http://www. iro. umontreal. ca/ bengioy/dlbook, 2014.
C. Bennett and A. I Rudnicky. The carnegie mellon communicator corpus, 2002.
D. Biber and E. Finegan. An initial typology of english text types. Corpus linguistics II: New studies in the analysis and exploitation of computer corpora, pages 19-46, 1986.
D. Biber and E. Finegan. Diachronic relations among speech-based and written registers in english. Variation in English: multi-dimensional studies, pages 66-83, 2001.
D. Bohus and A. I Rudnicky. Sorry, I didn’t catch that! In Recent Trends in Discourse and Dialogue, pages 123-154. Springer, 2008.
S. E. Brennan, K. S. Schuhmann, and K. M. Batres. Entrainment on the move and in the lab: The walking around corpus. In Proceedings of the 35th Annual Conference of the Cognitive Science Society, 2013.
G. Brown, A. Anderson, R. Shillcock, and G. Yule. Teaching talk. Cambridge: CUP, 1984.
Susanne Burger, Karl Weilhammer, Florian Schiel, and Hans G Tillmann. Verbmobil data collection and annotation. In Verbmobil: Foundations of speech-to-speech translation, pages 537-549. Springer, 2000.
J. E. Cahn and S. E. Brennan. A psychological model of grounding and repair in dialog. In AAAI Symposium on Psychological Models of Communication in Collaborative Systems, 1999.
A. Canavan and G. Zipperlen. Callfriend american english-non-southern dialect. Linguistic Data Consortium, 10: 1, 1996.
A. Canavan, D. Graff, and G. Zipperlen. Callhome american english speech. Linguistic Data Consortium, 1997.
S. K. Card, T. P. Moran, and A. Newell. The Psychology of Human-Computer Interaction. L. Erlbaum Associates Inc., Hillsdale, NJ, USA, 1983. ISBN 0898592437.
R. Carter. Orders of reality: Cancode, communication, and culture. ELT journal, 52 (1): 43-56, 1998.
R. Carter and M. McCarthy. Cambridge grammar of English: a comprehensive guide; spoken and written English grammar and usage. Ernst Klett Sprachen, 2006.
Tanya L. Chartrand and J. A. Bargh. The chameleon effect: the perception-behavior link and social interaction. Journal of personality and social psychology, 76 (6): 893, 1999.
T. Chen and M. Kan. Creating a live, public short message service corpus: the nus sms corpus. Language Resources and Evaluation, 47 (2): 299-335, 2013.
Y.-N. Chen, D. Hakkani-Tür, and X. He. Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on, pages 6045-6049. IEEE, 2016.
H. H. Clark and S. E. Brennan. Grounding in communication. Perspectives on socially shared cognition, 13: 127-149, 1991.
P. R. Cohen. If not turing's test, then what? AI magazine, 26 (4): 61, 2005.
K. M. Colby. Modeling a paranoid mind. Behavioral and Brain Sciences, 4: 515-534, 1981.
R. M. Cooper. The control of eye fixation by the meaning of spoken language: A new methodology for the real-time investigation of speech perception, memory, and language processing. Cognitive Psychology, 6 (1): 84-107, 1974.
N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines: And Other Kernel-based Learning Methods. Cambridge University Press, 2000.
H. Cuayáhuitl, S. Keizer, and O. Lemon. Strategic dialogue management via deep reinforcement learning. arXiv preprint arXiv:1511.08099, 2015.
C. Danescu-Niculescu-Mizil and L. Lee. Chameleons in imagined conversations: A new approach to understanding coordination of linguistic style in dialogs. In Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics, ACL, 2011.
L. Daubigney, M. Geist, S. Chandramohan, and O. Pietquin. A comprehensive reinforcement learning framework for dialogue management optimization. IEEE Journal of Selected Topics in Signal Processing, 6 (8): 891-902, 2012.
M. Davies. Comparing the corpus of american soap operas, COCA, and the BNC, 2012a.
M. Davies. Corpus of american soap operas, 2012b.
I. de Kok, D. Heylen, and L. Morency. Speaker-adaptive multimodal prediction model for listener responses. In Proceedings of the 15th ACM on International conference on multimodal interaction, 2013.
L. Deng and X. Li. Machine learning paradigms for speech recognition: An overview. Audio, Speech, and Language Processing, IEEE Transactions on, 21 (5): 1060-1089, 2013.
L. Denoyer and P. Gallinari. The wikipedia xml corpus. In Comparative Evaluation of XML Information Retrieval Systems, pages 12-19. Springer, 2007.
B. Dhingra, Z. Zhou, D. Fitzpatrick, M. Muehl, and W. Cohen. Tweet2vec: Character-based distributed representations for social media. arXiv preprint arXiv:1605.03481, 2016.
A. Djalali, S. Lauer, and C. Potts. Corpus evidence for preference-driven interpretation. In Logic, Language and Meaning, pages 150-159. Springer, 2012.
J. Dodge, A. Gane, X. Zhang, A. Bordes, S. Chopra, A. Miller, A. Szlam, and J. Weston. Evaluating prerequisite qualities for learning end-to-end dialog systems. arXiv preprint arXiv:1511.06931, 2015.
S. Dose. Flipping the script: A corpus of american television series (cats) for corpus-based language learning and teaching. Corpus Linguistics and Variation in English: Focus on Non-native Englishes, 2013.
E. Douglas-Cowie, R. Cowie, I. Sneddon, C. Cox, O. Lowry, M. Mcrorie, J. Martin, L. Devillers, S. Abrilian, A. Batliner, et al. The humaine database: addressing the collection and annotation of naturalistic and induced emotional data. In Affective computing and intelligent interaction, pages 488-500. Springer, 2007.
W. Eckert, E. Levin, and R. Pieraccini. User modeling for spoken dialogue system evaluation. In Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on, pages 80-87, 1997.
L. El Asri, H. Schulz, S. Sharma, J. Zumer, J. Harris, E. Fine, R. Mehrotra, and K. Suleman. Frames: Acorpus for adding memory to goal-oriented dialogue systems. preprint on webpage at http://www.maluuba.com/publications/, 2017.
M. Elsner and E. Charniak. You talking to me? a corpus and algorithm for conversation disentanglement. In Association for Computational Linguistics (ACL), 2008.
D. Erhan, Y. Bengio, A. Courville, Pierre-A. Manzagol, and P. Vincent. Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11, 2010.
G. Di Fabbrizio, G. Tur, and D. Hakkani-Tür. Bootstrapping spoken dialog systems with data reuse. In SIGDIAL, 2004.
M. Fatemi, L. E. Asri, H. Schulz, J. He, and K. Suleman. Policy networks with two-stage training for dialogue systems. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2016.
D. Fisher, M. Smith, and H. T Welser. You are who you talk to: Detecting roles in usenet newsgroups. In Proceedings of the 39th Annual Hawaii International Conference on System Sciences (HICSS'06), volume 3, pages 59b-59b, 2006.
P. Forchini. Spontaneity reloaded: American face-to-face and movie conversation compared. In Corpus Linguistics, 2009.
P. Forchini. Movie language revisited. Evidence from multi-dimensional analysis and corpora. Peter Lang, 2012.
G. Forgues, J. Pineau, J. Larchevêque, and R. Tremblay. Bootstrapping dialog systems with word embeddings. In Workshop on Modern Machine Learning and Natural Language Processing, Advances in neural information processing systems (NIPS), 2014.
E. N. Forsyth and C. H. Martell. Lexical and discourse analysis of online chat dialog. In International Conference on Semantic Computing (ICSC)., pages 19-26, 2007.
M. Galley, C. Brockett, A. Sordoni, Y. Ji, M. Auli, C. Quirk, M. Mitchell, J. Gao, and B. Dolan. deltaBLEU: A discriminative metric for generation tasks with intrinsically diverse targets. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL, pages 445-450, 2015.
M. Gasi\'c, F. Jurcícek, S. Keizer, F. Mairesse, B. Thomson, K. Yu, and S. Young. Gaussian processes for fast policy optimisation of pomdp-based dialogue managers. In Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 201-204. Association for Computational Linguistics, 2010.
M. Gasi\'c, F. Jurcícek, B. Thomson, K. Yu, and S. Young. On-line policy optimisation of spoken dialogue systems via live interaction with human subjects. In IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pages 312-317. IEEE, 2011.
M. Gasi\'c, M. Henderson, B. Thomson, P. Tsiakoulis, and S. Young. Policy optimisation of pomdp-based dialogue systems without state space compression. In Spoken Language Technology Workshop (SLT), 2012 IEEE, pages 31-36. IEEE, 2012.
M. Gasic, 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 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 8367-8371, 2013.
M. Gasi\'c, N. Mrksi\'c, L. M. Rojas-Barahona, P.-H. Su, S. Ultes, D. Vandyke, T.-H. Wen, and S. Young. Dialogue manager domain adaptation using gaussian process reinforcement learning. Computer Speech & Language, 2016.
A. Genevay and R. Laroche. Transfer learning for user adaptation in spoken dialogue systems. In Proceedings of the 2016 International Conference on Autonomous Agents & Multiagent Systems, pages 975-983. International Foundation for Autonomous Agents and Multiagent Systems, 2016.
K. Georgila, J. Henderson, and O. Lemon. User simulation for spoken dialogue systems: learning and evaluation. In Proceedings of INTERSPEECH, 2006.
K. Georgila, M. Wolters, J. D. Moore, and R. H. Logie. The MATCH corpus: A corpus of older and younger users’ interactions with spoken dialogue systems. Language Resources and Evaluation, 44 (3): 221-261, 2010.
J. Gibson and A. D. Pick. Perception of another person's looking behavior. The American journal of psychology, 76 (3): 386-394, 1963.
J. J Godfrey, E. C Holliman, and J McDaniel. SWITCHBOARD: Telephone speech corpus for research and development. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP-92), 1992.
I. Goodfellow, A. Courville, and Y. Bengio. Deep learning. Book in preparation for MIT Press, 2015. URL http://goodfeli.github.io/dlbook/.
C. Goodwin. Conversational Organization: Interaction Between Speakers and Hearers. New York: Academic Press, 1981.
A. L. Gorin, G. Riccardi, and J. H. Wright. How may I help you? Speech Communication, 23 (1): 113-127, 1997.
J. Gratch, N. Wang, J. Gerten, E. Fast, and R. Duffy. Creating Rapport with Virtual Agents. 7th International Conference on Intelligent Virtual Agents, Paris, France 2007.
A. Graves. Sequence transduction with recurrent neural networks. In Proceedings of the 29th International Conference on Machine Learning (ICML), Representation Learning Workshop, 2012.
A. Graves, G. Wayne, and I. Danihelka. Neural turing machines. arXiv preprint arXiv:1410.5401, 2014.
S. Greenbaum. Comparing English worldwide: The international corpus of English. Clarendon Press, 1996.
S. Greenbaum and G Nelson. The international corpus of english (ICE) project. World Englishes, 15 (1): 3-15, 1996.
C. Gülçehre, O. Firat, K. Xu, K. Cho, L. Barrault, H. Lin, F. Bougares, H. Schwenk, and Y. Bengio. On using monolingual corpora in neural machine translation. CoRR, abs/1503.03535, 2015.
I. Gurevych and M. Strube. Semantic similarity applied to spoken dialogue summarization. In Proceedings of the 20th international conference on Computational Linguistics, 2004.
V. Haslerud and A. Stenström. The bergen corpus of london teenager language (COLT). Spoken English on Computer. Transcription, Mark-up and Application. London: Longman, pages 235-242, 1995.
Peter A Heeman and James F Allen. The trains 93 dialogues. Technical report, DTIC Document, 1995.
C. T. Hemphill, J. J. Godfrey, and G. R. Doddington. The atis spoken language systems pilot corpus. In Proceedings of the DARPA speech and natural language workshop, pages 96-101, 1990.
M. Henderson, B. Thomson, and S. Young. Deep neural network approach for the dialog state tracking challenge. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2013.
M. Henderson, B. Thomson, and J. Williams. Dialog state tracking challenge 2 & 3, 2014a.
M. Henderson, B. Thomson, and J. Williams. The second dialog state tracking challenge. In SIGDIAL, 2014b.
M. Henderson, B. Thomson, and S. Young. Word-based dialog state tracking with recurrent neural networks. In 15th Special Interest Group on Discourse and Dialogue (SIGDIAL), page 292, 2014c.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T.a N. Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE, 29 (6): 82-97, 2012.
T. Hiraoka, G. Neubig, K. Yoshino, T. Toda, and S. Nakamura. Active learning for example-based dialog systems. In Proc Intl Workshop on Spoken Dialog Systems, Saariselka, Finland, 2016.
H. Hung and G. Chittaranjan. The IDIAP wolf corpus: exploring group behaviour in a competitive role-playing game. In Proceedings of the international conference on Multimedia, pages 879-882, 2010.
J. L. Hutchens and M. D. Alder. Introducing MegaHAL. In Proceedings of the Joint Conferences on New Methods in Language Processing and Computational Natural Language Learning, 1998.
Arne J. and Nils D. Talking to a computer is not like talking to your best friend. In Proceedings of The first Scandinivian Conference on Artificial Intelligence, 1988.
S. Jung, C. Lee, K. Kim, M. Jeong, and G. G. Lee. Data-driven user simulation for automated evaluation of spoken dialog systems. Computer Speech & Language, 23 (4): 479-509, 2009.
D. Jurafsky and J. H. Martin. Speech and language processing, 2nd Edition. Prentice Hall, 2008.
F. Jurccek, S. Keizer, M. Gasic, F. Mairesse, B. Thomson, K. Yu, and S. Young. Real user evaluation of spoken dialogue systems using amazon mechanical turk. In Proceedings of INTERSPEECH, volume 11, 2011.
R. Kadlec, M. Schmid, and J. Kleindienst. Improved deep learning baselines for ubuntu corpus dialogs. Neural Information Processing Systems Workshop on Machine Learning for Spoken Language Understanding, 2015.
S. Kim, L. F. D’Haro, R. E. Banchs, J. Williams, and M. Henderson. Dialog state tracking challenge 4, 2015.
S. Kim, L. F. D’Haro, R. E. Banchs, J. D. Williams, M. Henderson, and K. Yoshino. The fifth dialog state tracking challenge. In IEEE Spoken Language Technology Workshop (SLT), 2016.
J. A Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: applying collaborative filtering to usenet news. Communications of the ACM, 40 (3): 77-87, 1997.
A. Kumar, O. Irsoy, J. Su, J. Bradbury, R. English, B. Pierce, P. Ondruska, I. Gulrajani, and R. Socher. Ask me anything: Dynamic memory networks for natural language processing. Neural Information Processing Systems (NIPS), 2015.
Merja Kytö and Terry Walker. Guide to A corpus of English dialogues 1560-1760. Acta Universitatis Upsaliensis, 2006.
I. Langkilde and K. Knight. Generation that exploits corpus-based statistical knowledge. In Proceedings of the 36th Annual Meeting of the Association for Computational Linguistics and 17th International Conference on Computational Linguistics-Volume 1, pages 704-710. Association for Computational Linguistics, 1998.
G. Leech. 100 million words of english: the british national corpus (BNC). Language Research, 28 (1): 1-13, 1992.
E. Levin and R. Pieraccini. A stochastic model of computer-human interaction for learning dialogue strategies. In Eurospeech, volume 97, pages 1883-1886, 1997.
E. Levin, R. Pieraccini, and W. Eckert. Learning dialogue strategies within the markov decision process framework. In Automatic Speech Recognition and Understanding, 1997. Proceedings., 1997 IEEE Workshop on, pages 72-79. IEEE, 1997.
J. Li, M. Galley, C. Brockett, J. Gao, and B. Dolan. A diversity-promoting objective function for neural conversation models. arXiv preprint arXiv:1510.03055, 2015.
J. Li, M. Galley, C. Brockett, J. Gao, and Bill D. A persona-based neural conversation model. In ACL, pages 994-1003, 2016.
G. Lin and M. Walker. All the world's a stage: Learning character models from film. In AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 2011a.
G. I. Lin and M. A. Walker. All the world's a stage: Learning character models from film. In AIIDE, 2011b.
Y. Li, H. Su, X. Shen, W. Li, Z. Cao, & S. Niu. DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset. In IJCNLP, 2017.
C. Lord and M. Haith. The perception of eye contact. Attention, Perception, & Psychophysics, 16 (3): 413-416, 1974.
R. Lowe, N. Pow, I. Serban, and J. Pineau. The ubuntu dialogue corpus: A large dataset for research in unstructured multi-turn dialogue systems. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015a.
R. Lowe, N. Pow, I. V. Serban, L. Charlin, and J. Pineau. Incorporating unstructured textual knowledge sources into neural dialogue systems. Neural Information Processing Systems Workshop on Machine Learning for Spoken Language Understanding, 2015b.
R. Lowe, I. V. Serban, M. Noseworthy, L. Charlin, and J. Pineau. On the evaluation of dialogue systems with next utterance classification. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2016.
J. M. Lucas, F. Fernández, J. Salazar, J. Ferreiros, and R. San Segundo. Managing speaker identity and user profiles in a spoken dialogue system. In Procesamiento del Lenguaje Natural, number 43 in 1, pages 77-84, 2009.
B. MacWhinney and C. Snow. The child language data exchange system. Journal of child language, 12 (02): 271-295, 1985.
F. Mairesse and S. Young. Stochastic language generation in dialogue using factored language models. Computational Linguistics, 2014.
F. Mairesse, M. Gasi\'c, F. Jurcícek, S. Keizer, B. Thomson, K. Yu, and S. Young. Phrase-based statistical language generation using graphical models and active learning. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1552-1561. Association for Computational Linguistics, 2010.
C. D. Manning and H. Schütze. Foundations of statistical natural language processing. MIT press, 1999.
M. McCarthy. Spoken language and applied linguistics. Ernst Klett Sprachen, 1998.
S. McGlashan, N. Fraser, N. Gilbert, E. Bilange, P. Heisterkamp, and N. Youd. Dialogue management for telephone information systems. In Proceedings of the third conference on Applied natural language processing, pages 245-246. Association for Computational Linguistics, 1992.
G. McKeown, M. F Valstar, R. Cowie, and M. Pantic. The SEMAINE corpus of emotionally coloured character interactions. In Multimedia and Expo (ICME), 2010 IEEE International Conference on, pages 1079-1084, 2010.
T. Mikolov, M. Karafiát, L. Burget, J. Cernock\`y, and Sanjeev Khudanpur. Recurrent neural network based language model. In 11th Proceedings of INTERSPEECH, pages 1045-1048, 2010.
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013.
G. A. Miller. WordNet: a lexical database for english. Communications of the ACM, 38 (11): 39-41, 1995.
A. Misra, P. Anand, J. E. F. Tree, M. Walker. Using Summarization to Discover Argument Facets in Online Idealogical Dialog. In The North American Chapter of the Association for Computational Linguistics (NAACL), 2015.
K. Mo, S. Li, Y. Zhang, J. Li, and Q. Yang. Personalizing a dialogue system with transfer learning. arXiv preprint arXiv:1610.02891, 2016.
S. Mohan and J. Laird. Learning goal-oriented hierarchical tasks from situated interactive instruction. In AAAI, 2014.
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. Ms marco: A human generated machine reading comprehension dataset. arXiv preprint arXiv:1611.09268, 2016.
L Nio, S. Sakti, G. Neubig, T. Toda, and S. Nakamura. Conversation dialog corpora from television and movie scripts. In 17th Oriental Chapter of the International Committee for the Co-ordination and Standardization of Speech Databases and Assessment Techniques (COCOSDA), pages 1-4, 2014.
E. Nöth, A. Horndasch, F. Gallwitz, and J. Haas. Experiences with commercial telephone-based dialogue systems. it-Information Technology (vormals it+ ti), 46 (6/2004): 315-321, 2004.
C. Oertel, F. Cummins, J. Edlund, P. Wagner, and N. Campbell. D64: A corpus of richly recorded conversational interaction. Journal on Multimodal User Interfaces, 7 (1-2): 19-28, 2013.
A. H. Oh and A. I. Rudnicky. Stochastic language generation for spoken dialogue systems. In Proceedings of the 2000 ANLP/NAACL Workshop on Conversational systems-Volume 3, pages 27-32. Association for Computational Linguistics, 2000.
T. Paek. Reinforcement learning for spoken dialogue systems: Comparing strengths and weaknesses for practical deployment. In Proc. Dialog-on-Dialog Workshop, INTERSPEECH, 2006.
K. Papineni, S. Roukos, T Ward, and W Zhu. BLEU: a method for automatic evaluation of machine translation. In Proceedings of the 40th annual meeting on Association for Computational Linguistics (ACL), 2002.
A. N. Pargellis, H-K. J. Kuo, and C. Lee. An automatic dialogue generation platform for personalized dialogue applications. Speech Communication, 42 (3-4): 329-351, 2004. doi: 10.1016/j.specom.2003.10.003.
Rebecca Passonneau and Evaneet Sachar. Loqui human-human dialogue corpus (transcriptions and annotations). 2014.
D. Perez-Marin and I. Pascual-Nieto. Conversational Agents and Natural Language Interaction: Techniques and Effective Practices. IGI Global, 2011.
S. Petrik. Wizard of Oz Experiments on Speech Dialogue Systems. PhD thesis, Citeseer, 2004.
R. Pieraccini, D. Suendermann, K. Dayanidhi, and J. Liscombe. Are we there yet? research in commercial spoken dialog systems. In Text, Speech and Dialogue, pages 3-13, 2009.
O. Pietquin and H. Hastie. A survey on metrics for the evaluation of user simulations. The knowledge engineering review, 28 (01): 59-73, 2013.
B. Piot, M. Geist, and O. Pietquin. Imitation learning applied to embodied conversational agents. In 4th Workshop on Machine Learning for Interactive Systems (MLIS 2015), volume 43, 2015.
C. Potts. Goal-driven answers in the cards dialogue corpus. In Proceedings of the 30th west coast conference on formal linguistics, pages 1-20, 2012.
A. Ratnaparkhi. Trainable approaches to surface natural language generation and their application to conversational dialog systems. Computer Speech & Language, 16 (3): 435-455, 2002.
A. Raux, B. Langner, D. Bohus, A. W. Black, and M. Eskenazi. Let’s go public! taking a spoken dialog system to the real world. In Proceedings of INTERSPEECH. Citeseer, 2005.
N. Reithinger and M. Klesen. Dialogue act classification using language models. In EuroSpeech, 1997.
H. Ren, W. Xu, Y. Zhang, and Y. Yan. Dialog state tracking using conditional random fields. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2013.
S. Renals, T. Hain, and H. Bourlard. Recognition and understanding of meetings the AMI and AMIDA projects. In IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU), 2007.
R. Reppen and N. Ide. The american national corpus overall goals and the first release. Journal of English Linguistics, 32 (2): 105-113, 2004.
Jeff Rickel and W Lewis Johnson. Animated agents for procedural training in virtual reality: Perception, cognition, and motor control. Applied artificial intelligence, 13 (4-5): 343-382, 1999.
V. Rieser and O. Lemon. Natural language generation as planning under uncertainty for spoken dialogue systems. In Empirical methods in natural language generation, pages 105-120. Springer, 2010.
A. Ritter, C. Cherry, and B. Dolan. Unsupervised modeling of twitter conversations. In North American Chapter of the Association for Computational Linguistics (NAACL), 2010.
A. Ritter, C. Cherry, and W. B. Dolan. Data-driven response generation in social media. In Proceedings of the conference on Empirical Methods in Natural Language Processing, 2011.
Sara Rosenthal and Kathleen McKeown. I couldn’t agree more: The role of conversational structure in agreement and disagreement detection in online discussions. In 16th Annual Meeting of the Special Interest Group on Discourse and Dialogue, page 168, 2015.
S. Rosset and S. Petel. The ritel corpus-an annotated human-machine open-domain question answering spoken dialog corpus. In The International Conference on Language Resources and Evaluation (LREC), 2006.
A. Roy, C. Guinaudeau, H. Bredin, and C. Barras. TVD: a reproducible and multiply aligned tv series dataset. In The International Conference on Language Resources and Evaluation (LREC), volume 2, 2014.
J. Ruppenhofer, M. Ellsworth, M. R.L. Petruck, C. R. Johnson, and J. Scheffczyk. FrameNet II: Extended Theory and Practice. International Computer Science Institute, 2006. Distributed with the FrameNet data.
J. Schatzmann and S. Young. The hidden agenda user simulation model. IEEE transactions on audio, speech, and language processing, 17 (4): 733-747, 2009.
J. Schatzmann, K. Georgila, and S. Young. Quantitative evaluation of user simulation techniques for spoken dialogue systems. In 6th Special Interest Group on Discourse and Dialogue (SIGDIAL), 2005.
J. Schatzmann, B. Thomson, K. Weilhammer, . Ye, and S. Young. Agenda-based user simulation for bootstrapping a pomdp dialogue system. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers, pages 149-152, 2007.
J. N. Schrading. Analyzing domestic abuse using natural language processing on social media data. Master's thesis, Rochester Institute of Technology, United States, 2015. http://scholarworks.rit.edu/theses.
N. Schrading, C. O. Alm, R. Ptucha, and C. M. Homan. An analysis of domestic a.se discourse on reddit. In Empirical Methods in Natural Language Processing (EMNLP), 2015.
K. K. Schuler. VerbNet: A broad-coverage, comprehensive verb lexicon. PhD thesis, University of Pennsylvania, 2005. Paper AAI3179808.
I. V. Serban and J. Pineau. Text-based speaker identification for multi-participant open-domain dialogue systems. Neural Information Processing Systems Workshop on Machine Learning for Spoken Language Understanding, 2015.
I. V. Serban, A. Sordoni, Y. Bengio, A. Courville, and J. Pineau. Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Networks. In AAAI, 2016. In press.
I. V. Serban, T. Klinger, G. Tesauro, K. Talamadupula, B. Zhou, Y. Bengio, and A. Courville. Multiresolution recurrent neural networks: An application to dialogue response generation. In AAAI Conference, 2017a.
I. V. Serban, A. Sordoni, R. Lowe, L. Charlin, J. Pineau, A. Courville, and Y. Bengio. A hierarchical latent variable encoder-decoder model for generating dialogues. In AAAI Conference, 2017b.
S. Shaikh, T. Strzalkowski, G. A. Broadwell, J. Stromer-Galley, S. M. Taylor, and N. Webb. Mpc: A multi-party chat corpus for modeling social phenomena in discourse. In The International Conference on Language Resources and Evaluation (LREC), 2010.
L. Shang, Z. Lu, and H. Li. Neural responding machine for short-text conversation. arXiv preprint arXiv:1503.02364, 2015.
C. Shaoul and C. Westbury. A usenet corpus (2005-2009), 2009.
S. Sharma, J. He, K. Suleman, H. Schulz, and P. Bachman. Natural language generation in dialogue using lexicalized and delexicalized data. arXiv preprint arXiv:1606.03632, 2016.
B. A. Shawar and E. Atwell. Different measurements metrics to evaluate a chatbot system. In Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies, pages 89-96, 2007a.
B. A. Shawar and Eric Atwell. Chatbots: are they really useful? In LDV Forum, volume 22, pages 29-49, 2007b.
Elizabeth Shriberg, Raj Dhillon, Sonali Bhagat, Jeremy Ang, and Hannah Carvey. The icsi meeting recorder dialog act (mrda) corpus. Technical report, DTIC Document, 2004.
A. Simpson and N. M Eraser. Black box and glass box evaluation of the sundial system. In Third European Conference on Speech Communication and Technology, 1993.
S. Singh, D. Litman, M. Kearns, and M. Walker. Optimizing dialogue management with reinforcement learning: Experiments with the njfun system. Journal of Artificial Intelligence Research, pages 105-133, 2002.
S. P. Singh, M. J. Kearns, D. J. Litman, and M. A. Walker. Reinforcement learning for spoken dialogue systems. In Neural Information Processing Systems, 1999.
A. Sordoni, M. Galley, M. Auli, C. Brockett, Y. Ji, M. Mitchell, J. Nie, J. Gao, and B. Dolan. A neural network approach to context-sensitive generation of conversational responses. In Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT 2015), 2015.
A. Stenström, G. Andersen, and I. K. Hasund. Trends in teenage talk: Corpus compilation, analysis and findings, volume 8. J. Benjamins, 2002.
A. Stent, R. Prasad, and M. Walker. Trainable sentence planning for complex information presentation in spoken dialog systems. In Proceedings of the 42nd annual meeting on association for computational linguistics, page 79. Association for Computational Linguistics, 2004.
A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates, D. Jurafsky, P. Taylor, R. Martin, C. Van Ess-Dykema, and M. Meteer. Dialogue act modeling for automatic tagging and recognition of conversational speech. Computational linguistics, 26 (3): 339-373, 2000.
P.-H. Su, Y.-B. Wang, T.-H. Yu, and L.-S. Lee. A dialogue game framework with personalized training using reinforcement learning for computer-assisted language learning. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 8213-8217. IEEE, 2013.
P.-H. Su, D. Vandyke, M. Gasic, D. Kim, N. Mrksic, T.-H. Wen, and S. Young. Learning from real users: Rating dialogue success with neural networks for reinforcement learning in spoken dialogue systems. In INTERSPEECH, 2015.
P.-H. Su, M. Gasic, N. Mrksic, L. Rojas-Barahona, S. Ultes, D. Vandyke, T.-H. Wen, and S. Young. Continuously learning neural dialogue management. arXiv preprint arXiv:1606.02689, 2016.
S. Sukhbaatar, A. Szlam, J. Weston, and R. Fergus. End-to-end memory networks. In Neural Information Processing Systems (NIPS), 2015.
X. Sun, J. Lichtenauer, M. Valstar, A. Nijholt, and M. Pantic. A multimodal database for mimicry analysis. In Affective Computing and Intelligent Interaction, pages 367-376. Springer, 2011.
J. Svartvik. The London-Lund corpus of spoken English: Description and research. Number 82 in 1. Lund University Press, 1990.
B. Thomson and S. Young. Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems. Computer Speech & Language, 24 (4): 562-588, 2010.
J. Tiedemann. Parallel data, tools and interfaces in opus. In The International Conference on Language Resources and Evaluation (LREC), 2012.
David Traum and Jeff Rickel. Embodied agents for multi-party dialogue in immersive virtual worlds. In Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2, pages 766-773. ACM, 2002.
A. M. Turing. Computing machinery and intelligence. Mind, pages 433-460, 1950.
D. C Uthus and D. W Aha. The ubuntu chat corpus for multiparticipant chat analysis. In AAAI Spring Symposium: Analyzing Microtext, 2013.
J. Vandeventer, A. J. Aubrey, P. L. Rosin, and D. Marshall. 4d cardiff conversation database (4D CCDb): A 4D database of natural, dyadic conversations. In Proceedings of the 1st Joint Conference on Facial Analysis, Animation and Auditory-Visual Speech Processing (FAAVSP 2015), 2015.
D. Vandyke, P.-H. Su, M. Gasic, N. Mrksic, T.-H. Wen, and S. Young. Multi-domain dialogue success classifiers for policy training. In Automatic Speech Recognition and Understanding (ASRU), 2015 IEEE Workshop on, pages 763-770. IEEE, 2015.
O. Vinyals and Q. Le. A neural conversational model. arXiv preprint arXiv:1506.05869, 2015.
M. A. Walker, D. J. Litman, C. A. Kamm, and A. Abella. Paradise: A framework for evaluating spoken dialogue agents. In Proceedings of the eighth conference on European chapter of the Association for Computational Linguistics, pages 271-280, 1997.
M. A. Walker, O. C. Rambow, and M. Rogati. Training a sentence planner for spoken dialogue using boosting. Computer Speech & Language, 16 (3): 409-433, 2002.
M. A. Walker, R. Grant, J. Sawyer, G. I. Lin, N. Wardrip-Fruin, and M. Buell. Perceived or not perceived: Film character models for expressive nlg. In ICIDS, pages 109-121, 2011.
M. A Walker, G. I. Lin, and J. Sawyer. An annotated corpus of film dialogue for learning and characterizing character style. In The International Conference on Language Resources and Evaluation (LREC), pages 1373-1378, 2012a.
M. A Walker, J. E. F. Tree, P. Anand, R. Abbott, and J. King. A corpus for research on deliberation and debate. In The International Conference on Language Resources and Evaluation (LREC), pages 812-817, 2012b.
Z. Wang and O. Lemon. A simple and generic belief tracking mechanism for the dialog state tracking challenge: On the believability of observed information. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2013.
S. Webb. A corpus driven study of the potential for vocabulary learning through watching movies. International Journal of Corpus Linguistics, 15 (4): 497-519, 2010.
J. Weizenbaum. ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9 (1): 36-45, 1966.
T. Wen, M. Gasic, D. Kim, N. Mrksic, P. Su, D. Vandyke, and S. Young. Stochastic language generation in dialogue using recurrent neural networks with convolutional sentence reranking. Special Interest Group on Discourse and Dialogue (SIGDIAL), 2015.
T.-H. Wen, M. Gasic, N. Mrksic, L. M. Rojas-Barahona, P.-H. Su, D. Vandyke, and S. Young. Multi-domain neural network language generation for spoken dialogue systems. In NAACL HLT, 2016.
J. Weston. Dialog-based language learning. arXiv preprint arXiv:1604.06045, 2016.
J. Weston, S. Chopra, and A. Bordes. Memory networks. In International Conference on Learning Representations (ICLR), 2015.
J. Williams, A. Raux, D. Ramachandran, and A. Black. The dialog state tracking challenge. In Special Interest Group on Discourse and Dialogue (SIGDIAL), 2013.
J. D. Williams and S. Young. Partially observable markov decision processes for spoken dialog systems. Computer Speech & Language, 21 (2): 393-422, 2007.
J. D. Williams and G. Zweig. End-to-end lstm-based dialog control optimized with supervised and reinforcement learning. arXiv preprint arXiv:1606.01269, 2016.
M. Wolska, Q. B. Vo, D. Tsovaltzi, I. Kruijff-Korbayová, E. Karagjosova, H. Horacek, A. Fiedler, and C. Benzmüller. An annotated corpus of tutorial dialogs on mathematical theorem proving. In The International Conference on Language Resources and Evaluation (LREC), 2004.
Britta Wrede and Elizabeth Shriberg. Relationship between dialogue acts and hot spots in meetings. In Automatic Speech Recognition and Understanding, 2003. ASRU'03. 2003 IEEE Workshop on, pages 180-185. IEEE, 2003.
Yi Yang, Wen-tau Yih, and Christopher Meek. Wikiqa: A challenge dataset for open-domain question answering. In EMNLP, pages 2013-2018. Citeseer, 2015.
Z. Yang, B. Li, Y. Zhu, I. King, G. Levow, and H. Meng. Collection of user judgments on spoken dialog system with crowdsourcing. In Spoken Language Technology Workshop (SLT), 2010 IEEE, pages 277-282, 2010.
S. Young, M. Gasic, B. Thomson, and J. D. Williams. POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE, 101 (5): 1160-1179, 2013.
S. J. Young. Probabilistic methods in spoken-dialogue systems. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 358 (1769), 2000.
Justine Zhang, Ravi Kumar, Sujith Ravi, and Cristian Danescu-Niculescu-Mizil. Conversational flow in oxford-style debates. arXiv preprint arXiv:1604.03114, 2016.
1Although the model does not require intermediate labels, it consists of sub-components whose parameters are trained with different objective function. Therefore, strictly speaking, this is not an end-to-end model.
2Machine-machine dialogue corpora are not of interest to us, because they typically differ significantly from natural human language. Furthermore, user simulation models are outside the scope of this survey.
3For more information on dialogue hot spots and how they relate to dialogue acts, see [Wrede and Shriberg, 2003].
15Most of the largest technical support datasets are based on commercial technical support channels, which are proprietary and never released to the public for privacy reasons.

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