A hierarchical latent variable encoder-decoder model for generating dialogues

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Volumn , Issue , 2017, Pages 3295-3301

  Serban, Iulian Vlad ^a   Sordoni, Alessandro ^b   Lowe, Ryan ^c   Charlin, Laurent ^d   Pineau, Joelle ^c   Courville, Aaron ^a   Bengio, Yoshua ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

^b INSTITUTE FOR RESEARCH IN IMMUNOLOGY AND CANCER   (Canada)

^c MCGILL UNIVERSITY   (Canada)

^d HEC MONTRÉAL   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; EQUIVALENCE CLASSES; NEURAL NETWORKS; SIGNAL ENCODING; SPEECH RECOGNITION; STOCHASTIC MODELS; STOCHASTIC SYSTEMS;

ENCODER-DECODER; HIERARCHICAL STRUCTURES; HUMAN EVALUATION; LATENT VARIABLE; MODEL PERFORMANCE; RESPONSE GENERATION; SEQUENTIAL DATA; VARIABLE NUMBER;

NETWORK ARCHITECTURE;

EID: 85021671951     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (43)

1. Bangalore, S.; Di Fabbrizio, G.; and Stent, A. 2008. Learning the structure of task-driven human-human dialogs. IEEE Transactions on Audio, Speech, and Language Processing 16(7):1249-1259.
2. Bayer, J., and Osendorfer, C. 2014. Learning stochastic recurrent networks. In NIPS, Workshop on Advances in Variational Inference.
3. Bengio, Y.; Simard, P.; and Frasconi, P. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5(2):157-166.
4. Boulanger-Lewandowski, N.; Bengio, Y.; and Vincent, P. 2012. Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In ICML.
5. Bowman, S. R.; Vilnis, L.; Vinyals, O.; Dai, A. M.; Jozefowicz, R.; and Bengio, S. 2016. Generating sentences from a continuous space. CoNLL.
6. Chelba, C.; Mikolov, T.; Schuster, M.; Ge, Q.; Brants, T.; Koehn, P.; and Robinson, T. 2014. One billion word benchmark for measuring progress in statistical language modeling. In INTERSPEECH.
7. Cho, K., et al. 2014. Learning phrase representations using rnn encoder-decoder for statistical machine translation. In EMNLP.
8. Chung, J.; Kastner, K.; Dinh, L.; Goel, K.; Courville, A.; and Bengio, Y. 2015. A recurrent latent variable model for sequential data. In NIPS.
9. Crook, N.; Granell, R.; and Pulman, S. 2009. Unsupervised classification of dialogue acts using a dirichlet process mixture model. In SIGDIAL.
10. Denton, E. L.; Chintala, S.; Szlam, A.; and Fergus, R. 2015. Deep generative image models using a laplacian pyramid of adversarial networks. In NIPS.
11. Fabius, O., and van Amersfoort, J. R. 2015. Variational recurrent auto-encoders. ICLR, Workshop Papers.
12. Forgues, G.; Pineau, J.; Larchevêque, J.-M.; and Tremblay, R. 2014. Bootstrapping dialog systems with word embeddings. NIPS, Modern Machine Learning and Natural Language Processing Workshop.
13. Galley, M., et al. 2015. deltaBLEU: A discriminative metric for generation tasks with intrinsically diverse targets. In ACL.
14. Goodfellow, I.; Courville, A.; and Bengio, Y. 2015. Deep Learning. MIT Press.
15. Gorin, A. L.; Riccardi, G.; and Wright, J. H. 1997. How may i help you? Speech communication 23(1):113-127.
16. Graves, A. 2012. Sequence transduction with recurrent neural networks. In ICML, Representation Learning Workshop.
17. Gregor, K.; Danihelka, I.; Graves, A.; and Wierstra, D. 2015. DRAW: A recurrent neural network for image generation. In ICLR.
18. Hinton, G., 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.
19. Hochreiter, S., and Schmidhuber, J. 1997. Long short-term memory. Neural computation 9(8).
20. Kannan, A.; Kurach, K.; Ravi, S.; Kaufmann, T.; Tomkins, A.; Miklos, B.; Corrado, G.; Lukács, L.; Ganea, M.; et al. 2016. Smart reply: Automated response suggestion for email. In ACM SIGKDD.
21. Kingma, D., and Ba, J. 2015. Adam: A method for stochastic optimization. In ICLR.
22. Kingma, D. P., and Welling, M. 2014. Auto-encoding variational bayes. In ICLR.
23. Li, J.; Galley, M.; Brockett, C.; Gao, J.; and Dolan, B. 2016. A diversity-promoting objective function for neural conversation models. In NAACL.
24. Liu, C.-W.; Lowe, R.; Serban, I. V.; Noseworthy, M.; Charlin, L.; and Pineau, J. 2016. How NOT to evaluate your dialogue system: An empirical study of unsupervised evaluation metrics for dialogue response generation. In EMNLP.
25. Lowe, R.; Pow, N.; Serban, I.; and Pineau, J. 2015. The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems. In SIGDIAL.
26. Markoff, J., and Mozur, P. 2015. For sympathetic ear, more chinese turn to smartphone program. NY Times.
27. Mikolov, T., et al. 2010. Recurrent neural network based language model. In INTERSPEECH.
28. Mitchell, J., and Lapata, M. 2008. Vector-based models of semantic composition. In ACL, 236-244.
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. Rezende, D. J.; Mohamed, S.; and Wierstra, D. 2014. Stochastic backpropagation and approximate inference in deep generative models. In ICML.
31. Ritter, A.; Cherry, C.; and Dolan, W. B. 2011. Data-driven response generation in social media. In EMNLP.
32. Rus, V., and Lintean, M. 2012. A comparison of greedy and optimal assessment of natural language student input using word-to-word similarity metrics. In ACL, Building Educational Applications Workshop.
33. Serban, I. V.; Sordoni, A.; Bengio, Y.; Courville, A. C.; and Pineau, J. 2016. Building end-to-end dialogue systems using generative hierarchical neural network models. In AAAI.
34. Shaikh, S.; Strzalkowski, T.; Taylor, S.; and Webb, N. 2010. VCA: an experiment with a multiparty virtual chat agent. In ACL, Workshop on Companionable Dialogue Systems.
35. Singh, S.; Litman, D.; Kearns, M.; and Walker, M. 2002. Optimizing dialogue management with reinforcement learning: Experiments with the NJFun system. JAIR 16:105-133.
36. Sordoni, A.; Bengio, Y.; Vahabi, H.; Lioma, C.; Simonsen, J. G.; and Nie, J.-Y. 2015a. A hierarchical recurrent encoder-decoder for generative context-aware query suggestion. In CIKM.
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 NAACL-HLT.
38. Sutskever, I.; Vinyals, O.; and Le, Q. V. 2014. Sequence to sequence learning with neural networks. In NIPS.
39. Theano Development Team. 2016. Theano: A Python framework for fast computation of mathematical expressions. arXiv e-prints abs/1605.02688.
40. Vinyals, O., and Le, Q. 2015. A neural conversational model. ICML, Deep Learning Workshop.
41. Young, S.; Gasic, M.; Thomson, B.; and Williams, J. D. 2013. POMDP-based statistical spoken dialog systems: A review. Proceedings of the IEEE 101(5):1160-1179.
42. Young, S. 2000. Probabilistic methods in spoken-dialogue systems. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 358(1769).
43. Zhai, K., and Williams, J. D. 2014. Discovering latent structure in task-oriented dialogues. In ACL.