Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models

Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, Michel Galley


Abstract
Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers’ traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.
Anthology ID:
I17-1061
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
605–614
Language:
URL:
https://www.aclweb.org/anthology/I17-1061
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-1061.pdf