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Dialog state tracking: Open problems, challenge task, and recent work

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If you have a question about this talk, please contact Catherine Breslin.

In conversational systems, “dialog state tracking” means inferring the user’s goal from the conversation history up to the current turn. System responses are chosen based on the estimated dialog state, so accurate dialog state tracking is crucial to the performance of conversational systems. However, state tracking is non-trivial since input is received via the error-prone processes of speech recognition and language understanding, so the system is never sure of what the user has said.

In commercial systems, dialog state tracking is typically done with simple but rather effective hand-crafted rules. Over the past 10 years, the research community has developed several statistical methods for state tracking. These have been shown to improve the performance of conversational systems in lab settings, and have recently be tested in public deployments for the first time.

In this talk, I’ll explain the dialog state tracking problem, and current solutions in industry and research. I’ll then cover two recent public deployments of statistical state tracking, which revealed several unanticipated weaknesses in state-of-the-art methods for state tracking. This study led to the creation of the “Dialog state tracking challenge”, in which dialog data and evaluation tools were released; I’ll next cover results from the challenge, which included 27 state trackers from 9 teams. Finally, I’ll describe some recent work on dialog state tracking at Microsoft.

This talk is part of the CUED Speech Group Seminars series.

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