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The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Open accessAbstract
This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems. A prototype HIS system for the tourist information domain is evaluated and compared with a baseline MDP system using both user simulations and a live user trial. The results give strong support to the central contention that the POMDP-based framework is both a tractable and powerful approach to building more robust spoken dialogue systems.
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Cited by
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Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
A Diversity-Promoting Objective Function for Neural Conversation Models
Li, J., Galley, M., Brockett, C., Gao, J., & Dolan, B. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1014 · Cited by 1,926
A Persona-Based Neural Conversation Model
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p16-1094 · Cited by 898
A Survey on Dialogue Systems: Recent Advances and New Frontiers
Chen, H., Liu, X., Yin, D., & Tang, J. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3166054.3166058 · Cited by 579
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Mrkšić, N., Ó Séaghdha, D., Wen, T.-H., Thomson, B., & Young, S. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1163 · Cited by 459
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Metadata
- Authors
- Young, S., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Thomson, B., & Yu, K.
- Venue
- Computer Speech & Language
- Publisher
- Elsevier BV
- Published
- 2010-04
- Volume
- 24
- Issue
- 2
- Pages
- 150-174
- ISSN
- 0885-2308 (ISSN-L)
- OpenAlex
- W2115101920
- Cited by
- 481
- References
- 0
- FWCI
- 38.31
- Type
- journal article
- Language
- en
- Open access
- green
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- Young, S., Gašić, M., Keizer, S., Mairesse, F., Schatzmann, J., Thomson, B., & Yu, K. (2010). The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management. Computer Speech & Language. https://doi.org/10.1016/j.csl.2009.04.001
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