Paper: Spoken Dialogue Management Using Probabilistic Reasoning

ACL ID P00-1013
Title Spoken Dialogue Management Using Probabilistic Reasoning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2000
Authors

Spoken dialogue managers have benefited from using stochastic planners such as Markov Decision Processes (MDPs). How- ever, so far, MDPs do not handle well noisy and ambiguous speech utterances. We use a Partially Observable Markov Decision Pro- cess (POMDP)-style approach to generate dialogue strategies by inverting the notion of dialogue state; the state represents the user’s intentions, rather than the system state. We demonstrate that under the same noisy con- ditions, a POMDP dialogue manager makes fewer mistakes than an MDP dialogue man- ager. Furthermore, as the quality of speech recognition degrades, the POMDP dialogue manager automatically adjusts the policy.