@inproceedings{ye-etal-2025-cleme2,
title = "{CLEME}2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction",
author = "Ye, Jingheng and
Xu, Zishan and
Li, Yinghui and
Song, Linlin and
Zhou, Qingyu and
Zheng, Hai-Tao and
Shen, Ying and
Jiang, Wenhao and
Kim, Hong-Gee and
Liu, Ruitong and
Su, Xin and
Shan, Zifei",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.10/",
doi = "10.18653/v1/2025.acl-long.10",
pages = "204--222",
ISBN = "979-8-89176-251-0",
abstract = "The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME."
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<abstract>The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME.</abstract>
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%0 Conference Proceedings
%T CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction
%A Ye, Jingheng
%A Xu, Zishan
%A Li, Yinghui
%A Song, Linlin
%A Zhou, Qingyu
%A Zheng, Hai-Tao
%A Shen, Ying
%A Jiang, Wenhao
%A Kim, Hong-Gee
%A Liu, Ruitong
%A Su, Xin
%A Shan, Zifei
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F ye-etal-2025-cleme2
%X The paper focuses on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, which received little attention in previous studies. To bridge the gap, we introduce **CLEME2.0**, a reference-based metric describing four fundamental aspects of GEC systems: hit-correction, wrong-correction, under-correction, and over-correction. They collectively contribute to exposing critical qualities and locating drawbacks of GEC systems. Evaluating systems by combining these aspects also leads to superior human consistency over other reference-based and reference-less metrics. Extensive experiments on two human judgment datasets and six reference datasets demonstrate the effectiveness and robustness of our method, achieving a new state-of-the-art result. Our codes are released at https://github.com/THUKElab/CLEME.
%R 10.18653/v1/2025.acl-long.10
%U https://aclanthology.org/2025.acl-long.10/
%U https://doi.org/10.18653/v1/2025.acl-long.10
%P 204-222
Markdown (Informal)
[CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction](https://aclanthology.org/2025.acl-long.10/) (Ye et al., ACL 2025)
ACL
- Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, and Zifei Shan. 2025. CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 204–222, Vienna, Austria. Association for Computational Linguistics.