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arXiv · 2206.05399

Building a Personalized Dialogue System with Prompt-Tuning

Tomohito Kasahara·Daisuke Kawahara·Nguyen Tung·Shengzhe Li·Kenta Shinzato·Toshinori Sato
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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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Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta Shinzato, Toshinori Sato. 2022-06-11. Building a Personalized Dialogue System with Prompt-Tuning. https://arxiv.org/abs/2206.05399

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