Listener’s Social Identity Matters in Personalised Response Generation

Guanyi Chen, Yinhe Zheng, Yupei Du


Abstract
Personalised response generation enables generating human-like responses by means of assigning the generator a social identity. However, pragmatics theory suggests that human beings adjust the way of speaking based on not only who they are but also whom they are talking to. In other words, when modelling personalised dialogues, it might be favourable if we also take the listener’s social identity into consideration. To validate this idea, we use gender as a typical example of a social variable to investigate how the listener’s identity influences the language used in Chinese dialogues on social media. Also, we build personalised generators. The experiment results demonstrate that the listener’s identity indeed matters in the language use of responses and that the response generator can capture such differences in language use. More interestingly, by additionally modelling the listener’s identity, the personalised response generator performs better in its own identity.
Anthology ID:
2020.inlg-1.26
Volume:
Proceedings of the 13th International Conference on Natural Language Generation
Month:
December
Year:
2020
Address:
Dublin, Ireland
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
205–215
Language:
URL:
https://www.aclweb.org/anthology/2020.inlg-1.26
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.inlg-1.26.pdf