@inproceedings{mehri-eskenazi-2020-usr,
title = "{USR}: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation",
author = "Mehri, Shikib and
Eskenazi, Maxine",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.64",
doi = "10.18653/v1/2020.acl-main.64",
pages = "681--707",
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.",
}
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%0 Conference Proceedings
%T USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation
%A Mehri, Shikib
%A Eskenazi, Maxine
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F mehri-eskenazi-2020-usr
%X 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.
%R 10.18653/v1/2020.acl-main.64
%U https://aclanthology.org/2020.acl-main.64
%U https://doi.org/10.18653/v1/2020.acl-main.64
%P 681-707
Markdown (Informal)
[USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://aclanthology.org/2020.acl-main.64) (Mehri & Eskenazi, ACL 2020)
ACL