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Best Paper Award at SigDial 2015!

Uncertainty in Dialogue Systems

Another research goal is to improve how systems respond to uncertainty about a user’s meaning, by enabling them to use probabilistic inference and more flexible follow-up and clarification capabilities to resolve uncertainty over time.

Many dialogue systems track the status of an ongoing conversation using a single, non-probabilistic information state. This can make it difficult for systems to tolerate substantial uncertainty about a user’s meaning. In practice, when ambiguities arise, most systems either assume the correctness of the highest-ranked interpretation (which can lead to misunderstandings), or else require immediate clarification from the user (which can be tiresome and unnatural). In my Ph.D. research, I developed an improved methodology for modeling the evolving mental state of an uncertain dialogue agent.

Selected Publications