@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://preview.aclanthology.org/ingest_wac_2008/2022.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."
}