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Computer Science > Computation and Language

arXiv:2206.05399 (cs)
[Submitted on 11 Jun 2022]

Title:Building a Personalized Dialogue System with Prompt-Tuning

Authors: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.
Comments: Accepted to NAACL 2022 SRW
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2206.05399 [cs.CL]
  (or arXiv:2206.05399v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2206.05399

Submission history

From: Tomohito Kasahara [view email]
[v1] Sat, 11 Jun 2022 02:21:11 UTC (6,831 KB)
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