USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation

Shikib Mehri, Maxine Eskenazi


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
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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/2020.acl-main.64.pdf
Video:
 http://slideslive.com/38928829
Code
 shikib/usr
Data
USR-PersonaChatUSR-TopicalChatConvAI2Topical-Chat