Building a Personalized Dialogue System with Prompt-Tuning

Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, Toshinori Sato

研究成果: Conference contribution

抄録

Dialogue systems without consistent responses are not fascinating. 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 automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses using less computational resources than fine-tuning.

本文言語English
ホスト出版物のタイトルNAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics
ホスト出版物のサブタイトルHuman Language Technologies, Proceedings of the Student Research Workshop
出版社Association for Computational Linguistics (ACL)
ページ96-105
ページ数10
ISBN(電子版)9781955917735
出版ステータスPublished - 2022
イベントNAACL 2022 Student Research Workshop, SRW 2022, at 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022 - Seattle, United States
継続期間: 2022 7月 102022 7月 15

出版物シリーズ

名前NAACL 2022 - 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Student Research Workshop

Conference

ConferenceNAACL 2022 Student Research Workshop, SRW 2022, at 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2022
国/地域United States
CitySeattle
Period22/7/1022/7/15

ASJC Scopus subject areas

  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • 情報システム
  • ソフトウェア

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