@InProceedings{inaba-takahashi:2018:SIGdial,
  author    = {Inaba, Michimasa  and  Takahashi, Kenichi},
  title     = {Estimating User Interest from Open-Domain Dialogue},
  booktitle = {Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {32--40},
  abstract  = {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.},
  url       = {http://www.aclweb.org/anthology/W18-5004}
}