Name
Title | ||
|---|---|---|
Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.18653/V1/2022.NAACL-MAIN.155 | North American Chapter of the Association for Computational Linguistics (NAACL) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sanghwan Bae | 1 | 0 | 0.34 |
| Donghyun Kwak | 2 | 0 | 0.68 |
| Sungdong Kim | 3 | 0 | 0.68 |
| Donghoon Ham | 4 | 0 | 0.68 |
| Soyoung Kang | 5 | 0 | 0.68 |
| Sangwoo Lee | 6 | 53 | 15.00 |
| Woomyoung Park | 7 | 0 | 1.35 |