DeepListener: Harnessing Expected Utility to Guide
Clarification Dialog in Spoken Language Systems
Redmond, Washington 98052
Redmond, WA 98052
Author email: firstname.lastname@example.org, email@example.com
We describe research on endowing spoken language systems with the ability to consider the cost of misrecognition, and using that knowledge to guide clarification dialog about a user’s intentions. Our approach relies on coupling utility-directed policies for dialog with the ongoing Bayesian fusion of evidence obtained from multiple utterances recognized during an interaction. After describing the methodology, we review the operation of a prototype system called DeepListener. DeepListener considers evidence gathered about utterances over time to make decisions about the optimal dialog strategy or real-world action to take given uncertainties about a user’s intentions and the costs and benefits of different outcomes.
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Keywords: Bayesian user models, Hidden Markov Model, clarification dialog, joint activity, conversational systems, utility-directed conversation, dialog systems.
In: Proceedings of the 6th International Conference on Spoken Language Processing (ICSLP 2000), Beijing, November
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