The Hidden Information State Model: a practical framework for POMDP-based spoken dialogue management

Steve Young * Milica Gašić Simon Keizer François Mairesse Jost Schatzmann Blaise Thomson Kai Yu
* Auteur correspondant
Abstract : 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 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.
Keywords : Statistical dialogue systems POMDP Hidden Information State model
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Article dans une revue
Computer Speech and Language, Elsevier, 2009, 24 (2), pp.150. 〈10.1016/j.csl.2009.04.001〉
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Steve Young, Milica Gašić, Simon Keizer, François Mairesse, Jost Schatzmann, et al.. The Hidden Information State Model: a practical framework for POMDP-based spoken dialogue management. Computer Speech and Language, Elsevier, 2009, 24 (2), pp.150. 〈10.1016/j.csl.2009.04.001〉. 〈hal-00598186〉

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