Building a Personalized Dialogue System with Prompt-Tuning
Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, Toshinori Sato
Abstract
Dialogue systems without consistent responses are not attractive. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of the automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses with less computational resources than fine-tuning.- Anthology ID:
- 2022.naacl-srw.13
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
- Month:
- July
- Year:
- 2022
- Address:
- Hybrid: Seattle, Washington + Online
- Editors:
- Daphne Ippolito, Liunian Harold Li, Maria Leonor Pacheco, Danqi Chen, Nianwen Xue
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 96–105
- Language:
- URL:
- https://aclanthology.org/2022.naacl-srw.13
- DOI:
- 10.18653/v1/2022.naacl-srw.13
- Cite (ACL):
- Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, and Toshinori Sato. 2022. Building a Personalized Dialogue System with Prompt-Tuning. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop, pages 96–105, Hybrid: Seattle, Washington + Online. Association for Computational Linguistics.
- Cite (Informal):
- Building a Personalized Dialogue System with Prompt-Tuning (Kasahara et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.naacl-srw.13.pdf
- Data
- DailyDialog