@inproceedings{kasahara-etal-2022-building,
title = "Building a Personalized Dialogue System with Prompt-Tuning",
author = "Kasahara, Tomohito and
Kawahara, Daisuke and
Tung, Nguyen and
Li, Shengzhe and
Shinzato, Kenta and
Sato, Toshinori",
editor = "Ippolito, Daphne and
Li, Liunian Harold and
Pacheco, Maria Leonor and
Chen, Danqi and
Xue, Nianwen",
booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop",
month = jul,
year = "2022",
address = "Hybrid: Seattle, Washington + Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.naacl-srw.13",
doi = "10.18653/v1/2022.naacl-srw.13",
pages = "96--105",
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.",
}
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%0 Conference Proceedings
%T Building a Personalized Dialogue System with Prompt-Tuning
%A Kasahara, Tomohito
%A Kawahara, Daisuke
%A Tung, Nguyen
%A Li, Shengzhe
%A Shinzato, Kenta
%A Sato, Toshinori
%Y Ippolito, Daphne
%Y Li, Liunian Harold
%Y Pacheco, Maria Leonor
%Y Chen, Danqi
%Y Xue, Nianwen
%S Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop
%D 2022
%8 July
%I Association for Computational Linguistics
%C Hybrid: Seattle, Washington + Online
%F kasahara-etal-2022-building
%X 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.
%R 10.18653/v1/2022.naacl-srw.13
%U https://aclanthology.org/2022.naacl-srw.13
%U https://doi.org/10.18653/v1/2022.naacl-srw.13
%P 96-105
Markdown (Informal)
[Building a Personalized Dialogue System with Prompt-Tuning](https://aclanthology.org/2022.naacl-srw.13) (Kasahara et al., NAACL 2022)
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.