Abstract
This paper presents a chat-like conversational system, and that generates a reply by selecting an appropriate reply generating module. Such modules consist in selecting a sentence from an article of Web news, retrieving a definition sentence in Wikipedia, question-answering, and so on. A dialogue strategy corresponds to which reply generating module should be chosen according to a user input and the dialogue history, and is learned in the MDP framework. User evaluations showed that our system could learn an appropriate dialogue strategy, and perform natural dialogues.
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© 2016 Springer International Publishing Switzerland
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Shibata, T., Egashira, Y., Kurohashi, S. (2016). Chat-Like Conversational System Based on Selection of Reply Generating Module with Reinforcement Learning. In: Rudnicky, A., Raux, A., Lane, I., Misu, T. (eds) Situated Dialog in Speech-Based Human-Computer Interaction. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-21834-2_6
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DOI: https://doi.org/10.1007/978-3-319-21834-2_6
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