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
- Editors:
- Kazunori Komatani, Diane Litman, Kai Yu, Alex Papangelis, Lawrence Cavedon, Mikio Nakano
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 32–40
- Language:
- URL:
- https://aclanthology.org/W18-5004
- DOI:
- 10.18653/v1/W18-5004
- Cite (ACL):
- Michimasa Inaba and Kenichi Takahashi. 2018. Estimating User Interest from Open-Domain Dialogue. In Proceedings of the 19th Annual SIGdial Meeting on Discourse and Dialogue, pages 32–40, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Estimating User Interest from Open-Domain Dialogue (Inaba & Takahashi, SIGDIAL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/W18-5004.pdf