Kenichi Takahashi


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Estimating User Interest from Open-Domain Dialogue
Michimasa Inaba | Kenichi Takahashi
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue

Dialogue personalization is an important issue in the field of open-domain chat-oriented dialogue systems. If these systems could consider their users’ interests, user engagement and satisfaction would be greatly improved. This paper proposes a neural network-based method for estimating users’ interests from their utterances in chat dialogues to personalize dialogue systems’ responses. We introduce a method for effectively extracting topics and user interests from utterances and also propose a pre-training approach that increases learning efficiency. Our experimental results indicate that the proposed model can estimate user’s interest more accurately than baseline approaches.


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Neural Utterance Ranking Model for Conversational Dialogue Systems
Michimasa Inaba | Kenichi Takahashi
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue