Abstract
User simulation has become an important trend of research in the field of spoken dialog systems because collecting and annotating real man-machine interactions with users is often expensive and time consuming. Yet, such data are generally required for designing and assessing efficient dialog systems. The general problem of user simulation is thus to produce as many as necessary natural, various and consistent interactions from as few data as possible. In this paper, is proposed a user simulation method based on Bayesian Networks (BN) that is able to produce consistent interactions in terms of user goal and dialog history but also to simulate the grounding process that often appears in human-human interactions. The BN is trained on a database of 1234 human-machine dialogs in the TownInfo domain (a tourist information application). Experiments with a state-of-the-art dialog system (REALL-DUDE/DIPPER/OAA) have been realized and promising results are presented.
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References
Bos, J., Klein, E., Lemon, O., Oka, T.: DIPPER: Description and Formalisation of an Information-State Update Dialogue System Architecture. In: Proceedings of the 4th SIGDIAL Workshop on Discourse and Dialogue, pp. 115–124 (2003)
Cheyer, A., Martin, D.: The Open Agent Architecture. Journal of Autonomous Agents and Multi-Agent Systems (4), 143–148 (2001)
Clark, H., Schaefer, E.: Contributing to discourse. Cognitive Science 13, 259–294 (1989)
Eckert, W., Levin, E., Pieraccini, R.: User Modeling for Spoken Dialogue System Evaluation. In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 80–87 (1997)
Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, K.: The Lumiere Project: Bayesian User Modeling for Inferring the Goals and Needs of Software Users. In: Proc. of the 14th Conference on Uncertainty in Artifical Intelligence (July 1998)
Janarthanam, S., Lemon, O.: A Two-Tier Simulation Model for Reinforcement Learning of Adaptative Referring Expression Generation Policies. In: Proceedings of 10th SIGDIAL, pp. 120–123 (2009)
Lemon, O., Liu, X., Shapiro, D., Tollander, C.: Hierarchical Reinforcement Learning of Dialogue Policies in a Development Environment for Dialogue Systems: REALL-DUDE. In: 10th SemDial Workshop on the Semantics and Pragmatics of Dialogue; BRANDIAL 2006 (2006)
Levin, E., Pieraccini, R., Eckert, W.: A Stochastic Model of Human-Machine Interaction for Learning Dialog Strategies. IEEE Transactions on Speech and Audio Processing 8, 11–23 (2000)
López-Cózar, R., Callejas, Z., McTear, M.F.: Testing the performance of spoken dialogue systems by means of an artificially simulated user. Artificial Intelligence Review 26(4), 291–323 (2006)
Meng, H., Wai, C., Pieracinni, R.: The Use of Belief Networks for Mixed-Initiative Dialog Modeling. In: Proceedings of the 8th International Conference on Spoken Language Processing (ICSLP) (October 2000)
Pietquin, O.: A Probabilistic Description of Man-Machine Spoken Communication. In: Proceedings of the IEEE International Conference on Multimedia and Expo (ICME) (July 2005)
Pietquin, O.: Learning to Ground in Spoken Dialogue Systems. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 4, pp. 165–168 (2007)
Pietquin, O., Dutoit, T.: A Probabilistic Framework for Dialog Simulation and Optimal Strategy Learning. IEEE Transactions on Audio, Speech, and Language Processing 14, 589–599 (2006)
Pietquin, O., Dutoit, T.: Dynamic Bayesian Networks for NLU Simulation with Applications to Dialog Optimal Strategy Learning. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP) (May 2006)
Pietquin, O., Renals, S.: ASR System Modeling for Automatic Evaluation and Optimization of Dialogue Systems. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Orlando, USA, FL (May 2002)
Pietquin, O., Rossignol, S., Ianotto, M.: Training Bayesian Networks for Realistic Man-Machine Spoken Dialogue Simulation. In: Proceedings of the 1rst International Workshop on Spoken Dialogue Systems Technology (December 2009)
Schatzmann, J., Georgila, K., Young, S.: Quantitative Evaluation of User Simulation Techniques for Spoken Dialogue Systems. In: Proceedings of the 6th SIGDIAL, pp. 45–54 (2005)
Schatzmann, J., Thomson, B., Young, S.: Error Simulation for Training Statistical Dialogue Systems. In: Proceedings of the International Workshop on Automatic Speech Recognition and Understanding (ASRU), Kyoto, Japan (2007)
Schatzmann, J., Weilhammer, K., Stuttle, M., Young, S.: A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. Knowledge Engineering Review 21(2), 97–126 (2007)
Thomson, B., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Yu, K., Young, S.: User Study of the Bayesian Update of Dialogue State Approach to Dialogue Management. In: Proceedings of Interspeech (2008)
Vuurpijl, L., ten Bosch, L., Rossignol, S., Neumann, A., Pfleger, N., Engel, R.: Evaluation of multimodal dialog systems. In: Proceedings of the LREC Workshop on Multimodal Corpora (2004)
Williams, J.D., Young, S.: Partially Observable Markov Decision Processes for Spoken Dialog Systems. Computer Speech and Language 21, 231–422 (2007)
Young, S.: CUED Standard Dialogue Acts. Technical report, Cambridge University Engineering Dept (October 2007)
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Rossignol, S., Pietquin, O., Ianotto, M. (2010). Simulation of the Grounding Process in Spoken Dialog Systems with Bayesian Networks. In: Lee, G.G., Mariani, J., Minker, W., Nakamura, S. (eds) Spoken Dialogue Systems for Ambient Environments. IWSDS 2010. Lecture Notes in Computer Science(), vol 6392. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16202-2_10
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DOI: https://doi.org/10.1007/978-3-642-16202-2_10
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