Estimating User Interest from Open-Domain Dialogue

Michimasa Inaba, Kenichi Takahashi


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.
Anthology ID:
W18-5004
Volume:
Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venues:
SIGDIAL | WS
SIG:
SIGDIAL
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–40
Language:
URL:
https://www.aclweb.org/anthology/W18-5004
DOI:
10.18653/v1/W18-5004
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-5004.pdf