Building a Personalized Dialogue System with Prompt-Tuning

• URL: http://arxiv.org/abs/2206.05399v1
• Date: Sat, 11 Jun 2022 02:21:11 GMT
• Title: Building a Personalized Dialogue System with Prompt-Tuning
• Authors: Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, Toshinori Sato
• Abstract summary: We build a dialogue system that responds based on a given character setting (persona) We propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models.
• Score: 5.942602139622984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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