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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