Abstract
The lack of meaningful automatic evaluation metrics for dialog has impeded open-domain dialog research. Standard language generation metrics have been shown to be ineffective for evaluating dialog models. To this end, this paper presents USR, an UnSupervised and Reference-free evaluation metric for dialog. USR is a reference-free metric that trains unsupervised models to measure several desirable qualities of dialog. USR is shown to strongly correlate with human judgment on both Topical-Chat (turn-level: 0.42, system-level: 1.0) and PersonaChat (turn-level: 0.48 and system-level: 1.0). USR additionally produces interpretable measures for several desirable properties of dialog.- Anthology ID:
- 2020.acl-main.64
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 681–707
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.64
- DOI:
- 10.18653/v1/2020.acl-main.64
- Cite (ACL):
- Shikib Mehri and Maxine Eskenazi. 2020. USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 681–707, Online. Association for Computational Linguistics.
- Cite (Informal):
- USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation (Mehri & Eskenazi, ACL 2020)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/2020.acl-main.64.pdf
- Code
- shikib/usr
- Data
- USR-PersonaChat, USR-TopicalChat, ConvAI2, Topical-Chat