Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models

• URL: http://arxiv.org/abs/2205.00176v1
• Date: Sat, 30 Apr 2022 06:23:06 GMT
• Title: Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models
• Authors: Sanghwan Bae, Donghyun Kwak, Sungdong Kim, Donghoon Ham, Soyoung Kang, Sang-Woo Lee, Woomyoung Park
• Abstract summary: We study the challenge of imposing roles on open-domain dialogue systems. We propose an efficient data collection framework for building role-satisfying dialogue dataset from scratch. Our models return few out-of-bounds utterances, keeping competitive performance on general metrics.
• Score: 15.062014096238803
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent open-domain dialogue models have brought numerous breakthroughs. However, building a chat system is not scalable since it often requires a considerable volume of human-human dialogue data, especially when enforcing features such as persona, style, or safety. In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans. To accomplish this, the system must satisfy a role specification that includes certain conditions on the stated features as well as a system policy on whether or not certain types of utterances are allowed. For this, we propose an efficient data collection framework leveraging in-context few-shot learning of large-scale language models for building role-satisfying dialogue dataset from scratch. We then compare various architectures for open-domain dialogue systems in terms of meeting role specifications while maintaining conversational abilities. Automatic and human evaluations show that our models return few out-of-bounds utterances, keeping competitive performance on general metrics. We release a Korean dialogue dataset we built for further research.

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