A Personalized Dialogue Generator with Implicit User Persona Detection

Itsugun Cho, Dongyang Wang, Ryota Takahashi, Hiroaki Saito


Abstract
Current works in the generation of personalized dialogue primarily contribute to the agent presenting a consistent personality and driving a more informative response. However, we found that the generated responses from most previous models tend to be self-centered, with little care for the user in the dialogue. Moreover, we consider that human-like conversation is essentially built based on inferring information about the persona of the other party. Motivated by this, we propose a novel personalized dialogue generator by detecting an implicit user persona. Because it is hard to collect a large number of detailed personas for each user, we attempted to model the user’s potential persona and its representation from dialogue history, with no external knowledge. The perception and fader variables were conceived using conditional variational inference. The two latent variables simulate the process of people being aware of each other’s persona and producing a corresponding expression in conversation. Finally, posterior-discriminated regularization was presented to enhance the training procedure. Empirical studies demonstrate that, compared to state-of-the-art methods, our approach is more concerned with the user’s persona and achieves a considerable boost across both automatic metrics and human evaluations.
Anthology ID:
2022.coling-1.29
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
367–377
Language:
URL:
https://aclanthology.org/2022.coling-1.29
DOI:
Bibkey:
Cite (ACL):
Itsugun Cho, Dongyang Wang, Ryota Takahashi, and Hiroaki Saito. 2022. A Personalized Dialogue Generator with Implicit User Persona Detection. In Proceedings of the 29th International Conference on Computational Linguistics, pages 367–377, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Personalized Dialogue Generator with Implicit User Persona Detection (Cho et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.29.pdf