LEARNING USER INTENTIONS IN SPOKEN DIALOGUE SYSTEMS

Hamid R. Chinaei, Brahim Chaib-draa, Luc Lamontagne

2009

Abstract

A common problem in spoken dialogue systems is finding the intention of the user. This problem deals with obtaining one or several topics for each transcribed, possibly noisy, sentence of the user. In this work, we apply the recent unsupervised learning method, Hidden Topic Markov Models (HTMM), for finding the intention of the user in dialogues. This technique combines two methods of Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM) in order to learn topics of documents. We show that HTMM can be also used for obtaining intentions for the noisy transcribed sentences of the user in spoken dialogue systems. We argue that in this way we can learn possible states in a speech domain which can be used in the design stage of its spoken dialogue system. Furthermore, we discuss that the learned model can be augmented and used in a POMDP (Partially Observable Markov Decision Process) dialogue manager of the spoken dialogue system.

References

  1. Atrash, A. and Pineau, J. (2006). Efficient planning and tracking in pomdps with large observation spaces. In AAAI-06 Workshop on Empirical and Statistical Approaches for Spoken Dialogue Systems.
  2. Blei, D. M. and Moreno, P. J. (2001). Topic segmentation with an aspect hidden Markov model. In Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval (SIGIR 7801), pages 343-348.
  3. Church, K. W. (1988). A stochastic parts program and noun phrase parser for unrestricted text. In Proceedings of the second conference on Applied Natural Language Processing (ANLP 7888), pages 136-143, Morristown, NJ, USA.
  4. Doshi, F. and Roy, N. (2007). Efficient model learning for dialog management. In Proceedings of the ACM/IEEE international conference on Human-Robot Interaction (HRI 7807), pages 65-72.
  5. Griffiths, T. and Steyvers, J. (2004). Finding scientific topics. Proceedings of the National Academy of Science, 101:5228-5235.
  6. Gruber, A., Rosen-Zvi, M., and Weiss, Y. (2007). Hidden Topic Markov Models. In Artificial Intelligence and Statistics (AISTATS 7807), San Juan, Puerto Rico.
  7. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the fifteenth conference on Uncertainty in Artificial Intelligence (UAI 7899), pages 289-296.
  8. Levin, E., Pieraccini, R., and Eckert, W. (1997). Learning dialogue strategies within the Markov decision process framework. 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, pages 72-79.
  9. Ortiz, L. E. and Kaelbling, L. P. (1999). Accelerating EM: An empirical study. In Proceedings of the fifteenth conference on Uncertainty in Artificial Intelligence (UAI 7899), pages 512-521, Stockholm, Sweden.
  10. Paek, Tim, Chickering, and David (2006). Evaluating the Markov assumption in Markov Decision Processes for spoken dialogue management. Language Resources and Evaluation, 40(1):47-66.
  11. Pietquin, O. and Dutoit, T. (2006). A probabilistic framework for dialog simulation and optimal strategy learning. IEEE Transactions on Audio, Speech, and Language Processing, 14(2):589-599.
  12. Pineau, J. and Atrash, A. (2007). Smartwheeler: A robotic wheelchair test-bed for investigating new models of human-robot interaction. In AAAI Spring Symposium on Multidisciplinary Collaboration for Socially Assistive Robotics.
  13. Rabiner, L. R. (1990). A tutorial on hidden markov models and selected applications in speech recognition. pages 267-296.
  14. Singh, S. P., Kearns, M. J., Litman, D. J., and Walker, M. A. (2000). Empirical evaluation of a reinforcement learning spoken dialogue system. In Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, pages 645-651. AAAI Press / The MIT Press.
  15. Walker, M. and Passonneau, R. (2001). DATE: a dialogue act tagging scheme for evaluation of spoken dialogue systems. In Proceedings of the first international conference on Human Language Rechnology research (HLT 7801), pages 1-8, Morristown, NJ, USA. Association for Computational Linguistics.
  16. Walker, M. A. (2000). An application of reinforcement learning to dialogue strategy selection in a spoken dialogue system for email. Journal of Artificial Intelligence Research (JAIR), 12:387-416.
  17. Walker, M. A., Litman, D. J., Kamm, A. A., and Abella, A. (1997). PARADISE: A Framework for Evaluating Spoken Dialogue Agents. In Proceedings of the ThirtyFifth Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics, pages 271-280, Somerset, New Jersey. Association for Computational Linguistics.
  18. Walker, M. A., Passonneau, R. J., and Boland, J. E. (2001). Quantitative and qualitative evaluation of darpa communicator spoken dialogue systems. In Meeting of the Association for Computational Linguistics, pages 515-522.
  19. Weilhammer, K., Williams, J. D., and Young, S. (2004). The SACTI-2 Corpus: Guide for Research Users, Cambridge University. Technical report.
  20. Williams, J. D., Poupart, P., and Young, S. (2005). Factored partially observable markov decision processes for dialogue management. In The 4th IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, Edinburgh, Scotland.
  21. Williams, J. D. and Young, S. (2004). Characterizing taskoriented dialog using a simulated asr channel. In Proceedings of International Conference on Spoken Language Processing (ICSLP 7804), Jeju, South Korea.
  22. Williams, J. D. and Young, S. (2007). Partially observable markov decision processes for spoken dialog systems. Computer Speech and Language, 21:393-422.
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Paper Citation


in Harvard Style

R. Chinaei H., Chaib-draa B. and Lamontagne L. (2009). LEARNING USER INTENTIONS IN SPOKEN DIALOGUE SYSTEMS . In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 107-114. DOI: 10.5220/0001663801070114


in Bibtex Style

@conference{icaart09,
author={Hamid R. Chinaei and Brahim Chaib-draa and Luc Lamontagne},
title={LEARNING USER INTENTIONS IN SPOKEN DIALOGUE SYSTEMS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={107-114},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001663801070114},
isbn={978-989-8111-66-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - LEARNING USER INTENTIONS IN SPOKEN DIALOGUE SYSTEMS
SN - 978-989-8111-66-1
AU - R. Chinaei H.
AU - Chaib-draa B.
AU - Lamontagne L.
PY - 2009
SP - 107
EP - 114
DO - 10.5220/0001663801070114