SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation

Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li, Ting Liu


Abstract
This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train SelF-Eval with a multi-level contrastive learning schema which helps to distinguish different score levels. Experimental results on multiple benchmarks show that SelF-Eval is highly consistent with human evaluations and better than the state-of-the-art models. We give a detailed analysis of the experiments in this paper. Our code is available on GitHub.
Anthology ID:
2022.coling-1.39
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
485–495
Language:
URL:
https://aclanthology.org/2022.coling-1.39
DOI:
Bibkey:
Cite (ACL):
Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li, and Ting Liu. 2022. SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation. In Proceedings of the 29th International Conference on Computational Linguistics, pages 485–495, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
SelF-Eval: Self-supervised Fine-grained Dialogue Evaluation (Ma et al., COLING 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.coling-1.39.pdf
Code
 royny/self-eval
Data
ConvAI2DailyDialogDailyDialog++FED