Abstract
Existing automatic evaluation systems of chatbots mostly rely on static chat scripts as ground truth, which is hard to obtain, and requires access to the models of the bots as a form of “white-box testing”. Interactive evaluation mitigates this problem but requires human involvement. In our work, we propose an interactive chatbot evaluation framework in which chatbots compete with each other like in a sports tournament, using flexible scoring metrics. This framework can efficiently rank chatbots independently from their model architectures and the domains for which they are trained.- Anthology ID:
- 2022.acl-long.522
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7579–7590
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.522
- DOI:
- 10.18653/v1/2022.acl-long.522
- Cite (ACL):
- Ruolan Yang, Zitong Li, Haifeng Tang, and Kenny Zhu. 2022. ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 7579–7590, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- ChatMatch: Evaluating Chatbots by Autonomous Chat Tournaments (Yang et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.acl-long.522.pdf
- Code
- ruolanyang/chatmatch