Building end-To-end dialogue systems using generative hierarchical neural network models

30th AAAI Conference on Artificial Intelligence, AAAI 2016

Volumn , Issue , 2016, Pages 3776-3783

  Serban, Iulian V ^a   Sordoni, Alessandro ^a   Bengio, Yoshua ^a   Courville, Aaron ^a   Pineau, Joelle ^b  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; LINGUISTICS; SPEECH PROCESSING;

DIALOGUE SYSTEMS; ENCODER-DECODER; GENERATIVE MODEL; HIERARCHICAL NEURAL NETWORKS; LANGUAGE MODEL; QUESTION-ANSWER PAIRS; STATE OF THE ART; SYSTEM RESPONSE;

HIERARCHICAL SYSTEMS;

EID: 84980367197     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (41)

1. Ameixa, D.; Coheur, L.; Fialho, P.; and Quaresma, P. 2014. Luke, I am your father: dealing with out-of-domain requests by using movies subtitles. In Intelligent Virtual Agents, 13- 21.
2. Auli, M.; Galley, M.; Quirk, C.; and Zweig, G. 2013. Joint language and translation modeling with recurrent neural networks. In Conference on Empirical Methods in Natural Language Processing, 1044-1054.
3. Bahdanau, D.; Cho, K.; and Bengio, Y. 2015. Neural machine translation by jointly learning to align and translate. In International Conference on Learning Representations.
4. Banchs, R. E., and Li, H. 2012. IRIS: A chat-oriented dialogue system based on the vector space model. In Proceedings of the Association for Computational Linguistics, System Demonstrations, 37-42.
5. Banchs, R. E. 2012. Movie-DiC: A movie dialogue corpus for research and development. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, 203-207.
6. Bastien, F.; Lamblin, P.; Pascanu, R.; Bergstra, J.; Goodfellow, I. J.; Bergeron, A.; Bouchard, N.; and Bengio, Y. 2012. Theano: new features and speed improvements. Conference on Neural Information Processing Systems, Workshop on Deep Learning and Unsupervised Feature Learning.
7. Bengio, Y.; Ducharme, R.; Vincent, P.; and Jauvin, C. 2003. A neural probabilistic language model. Journal of Machine Learning Research 3:1137-1155.
8. Bengio, Y. 2012. Practical recommendations for gradientbased training of deep architectures. In Tricks of the Trade, Second Edition, volume 7700 of Theoretical Computer Science and General Issues. Springer-Verlag. 437-478.
9. Bird, S.; Klein, E.; and Loper, E. 2009. Natural Language Processing with Python. O'Reilly Media.
10. Cantrell, R.; Talamadupula, K.; Schermerhorn, P.; Benton, J.; Kambhampati, S.; and Scheutz, M. 2012. Tell me when and why to do it! Run-Time planner model updates via natural language instruction. In ACM/IEEE International Conference on Human-Robot Interaction, 471-478.
11. Cho, K.; van Merrienboer, B.; Gulcehre, C.; Bahdanau, D.; Bougares, F.; Schwenk, H.; and Bengio, Y. 2014. Learning phrase representations using RNN Encoder-Decoder for statistical machine translation. In Conference on Empirical Methods in Natural Language Processing, 1724-1734.
12. Clark, H. H., and Brennan, S. E. 1991. Grounding in communication. Perspectives on socially shared cognition 13(1991):127-149.
13. Deng, L., and Li, X. 2013. Machine learning paradigms for speech recognition: An overview. Audio, Speech, and Language Processing, IEEE Transactions on 21(5):1060-1089.
14. Forchini, P. 2009. Spontaneity reloaded: American faceto- face and movie conversation compared. In Corpus Linguistics 2009. Abstracts of the Corpus Linguistics Conference, 118.
15. Galley, M.; Brockett, C.; Sordoni, A.; Ji, Y.; Auli, M.; Quirk, C.; Mitchell, M.; Gao, J.; and Dolan, B. 2015. Delta BLEU: A discriminative metric for generation tasks with intrinsically diverse targets. In Proceedings of the Annual Meeting of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (Short Papers), 445-450.
16. Gasic, M.; Breslin, C.; Henderson, M.; Kim, D.; Szummer, M.; Thomson, B.; Tsiakoulis, P.; and Young, S. 2013. Online policy optimisation of Bayesian spoken dialogue systems via human interaction. In IEEE International Conference on Acoustics, Speech and Signal Processing, 8367-8371.
17. Goodfellow, I. J.; Warde-Farley, D.; Mirza, M.; Courville, A.; and Bengio, Y. 2013. Maxout networks. Proceedings of the International Conference on Machine Learning 1319- 1327.
18. Goodman, J. T. 2001. A bit of progress in language modeling extended version. Machine Learning and Applied Statistics Group Microsoft Research. Technical Report, MSR-TR- 2001-72.
19. Graves, A. 2012. Sequence transduction with recurrent neural networks. In Proceedings of the International Conference on Machine Learning, Representation Learning Workshop.
20. Henderson, M.; Thomson, B.; and Young, S. 2014. Wordbased dialog state tracking with recurrent neural networks. In Annual Meeting of the Special Interest Group on Discourse and Dialogue, 292-299.
21. Hinton, G.; Deng, L.; Yu, D.; Dahl, G. E.; Mohamed, A.-r.; Jaitly, N.; Senior, A.; Vanhoucke, V.; Nguyen, P.; Sainath, T. N.; et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE 29(6):82-97.
22. Kingma, D., and Ba, J. 2014. Adam: A method for stochastic optimization. In International Conference for Learning Representations.
23. Kiros, R.; Salakhutdinov, R.; and Zemel, R. 2014. Multimodal neural language models. In Proceedings of the International Conference on Machine Learning, 595-603.
24. Langley, P.; Meadows, B.; Gabaldon, A.; and Heald, R. 2014. Abductive understanding of dialogues about joint activities. Interaction Studies 15(3):426-454.
25. Li, J.; Galley, M.; Brockett, C.; Gao, J.; and Dolan, B. 2015. A diversity-promoting objective function for neural conversation models. CoRR abs/1510.03055.
26. Mikolov, T.; Karafiát, M.; Burget, L.; Cernockỳ, J.; and Khudanpur, S. 2010. Recurrent neural network based language model. In Annual Conference of the International Speech Communication Association, 1045-1048.
27. Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G. S.; and Dean, J. 2013. Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems, 3111-3119.
28. Mohan, S., and Laird, J. 2014. Learning goal-oriented hierarchical tasks from situated interactive instruction. In AAAI Conference on Artificial Intelligence, 387-394.
29. Pietquin, O., and Hastie, H. 2013. A survey on metrics for the evaluation of user simulations. The Knowledge Engineering Review 28(01):59-73.
30. Ritter, A.; Cherry, C.; and Dolan, B. 2010. Unsupervised modeling of Twitter conversations. In The Annual Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies, 172-180.
31. Ritter, A.; Cherry, C.; and Dolan, W. B. 2011. Data-driven response generation in social media. In Proceedings of the Empiricial Methods in Natural Language, 583-593.
32. Schatzmann, J.; Georgila, K.; and Young, S. 2005. Quantitative evaluation of user simulation techniques for spoken dialogue systems. In Special Interest Group on Discourse and Dialogue Workshop on Discourse and Dialogue.
33. Shang, L.; Lu, Z.; and Li, H. 2015. Neural responding machine for short-Text conversation. In Association for Computational Linguistics, 1577-1586.
34. Shawar, B. A., and Atwell, E. 2007. Chatbots: Are they really useful? In LDV Forum, volume 22, 29-49.
35. Singh, S.; Litman, D.; Kearns, M.; and Walker, M. 2002. Optimizing dialogue management with reinforcement learning: Experiments with the NJFun system. Journal of Artificial Intelligence Research 105-133.
36. Sordoni, A.; Bengio, Y.; Vahabi, H.; Lioma, C.; Simonsen, J. G.; and Nie, J.-Y. 2015a. A hierarchical recurrent encoderdecoder for generative context-Aware query suggestion. In Proceedings of the ACM International Conference on Information and Knowledge Management, 553-562.
37. Sordoni, A.; Galley, M.; Auli, M.; Brockett, C.; Ji, Y.; Mitchell, M.; Nie, J.-Y.; Gao, J.; and Dolan, B. 2015b. A neural network approach to context-sensitive generation of conversational responses. In Conference of the North American Chapter of the Association for Computational Linguistics, 196-205.
38. Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014. Sequence to sequence learning with neural networks. In Advances In Neural Information Processing Systems, 3104-3112.
39. Vinyals, O., and Le, Q. 2015. A neural conversational model. Proceedings of the International Conference on Machine Learning, Deep Learning Workshop.
40. Vinyals, O.; Toshev, A.; Bengio, S.; and Erhan, D. 2015. Show and tell: A neural image caption generator. In The IEEE Conference on Computer Vision and Pattern Recognition, 2155-2162.
41. Young, S.; Gasic, M.; Thomson, B.; and Williams, J. D. 2013. POMDP-based statistical spoken dialog systems: A review. IEEE 101(5):1160-1179.