Optimal dialog in consumer-rating systems using a POMDP framework

Authors:
Zhifei Li;Patrick Nguyen;Geoffrey Zweig
Affiliations:
Johns Hopkins University, Baltimore, MD;Microsoft Corporation, Redmond, WA;Microsoft Corporation, Redmond, WA
Venue:
SIGdial '08 Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue
Year:
2008

Citing 4
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Voice-rate: a dialog system for consumer ratings

NAACL-Demonstrations '07 Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations
Perseus: randomized point-based value iteration for POMDPs

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Abstract

Voice-Rate is an experimental dialog system through which a user can call to get product information. In this paper, we describe an optimal dialog management algorithm for Voice-Rate. Our algorithm uses a POMDP framework, which is probabilistic and captures uncertainty in speech recognition and user knowledge. We propose a novel method to learn a user knowledge model from a review database. Simulation results show that the POMDP system performs significantly better than a deterministic baseline system in terms of both dialog failure rate and dialog interaction time. To the best of our knowledge, our work is the first to show that a POMDP can be successfully used for disambiguation in a complex voice search domain like Voice-Rate.