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Dialogue Act Prediction Using Stochastic Context-Free Grammar Induction

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If you have a question about this talk, please contact Johanna Geiss.

“In this talk I will describe a model-based approach to dialogue management, which is guided by data-driven dialogue act prediction. The statistical prediction is based on stochastic context-free grammars that have been obtained by means of grammatical inference. The dialogue act prediction is explored both for dialogue acts without realised semantic content (consisting only of communicative functions) and for dialogue acts with realised semantic content. The approach improves over several n-gram language models and can be used in isolation or for user simulation in reinforcement learning.”

This talk is part of the NLIP Seminar Series series.

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