%0 Conference Proceedings %T Estimating User Interest from Open-Domain Dialogue %A Inaba, Michimasa %A Takahashi, Kenichi %Y Komatani, Kazunori %Y Litman, Diane %Y Yu, Kai %Y Papangelis, Alex %Y Cavedon, Lawrence %Y Nakano, Mikio %S Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue %D 2018 %8 July %I Association for Computational Linguistics %C Melbourne, Australia %F inaba-takahashi-2018-estimating %X 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. %R 10.18653/v1/W18-5004 %U https://aclanthology.org/W18-5004 %U https://doi.org/10.18653/v1/W18-5004 %P 32-40