CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction

Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou +7 more
7/1/2024
cs.CL

Official code: THUKElab/CLEME (12 stars, Apache-2.0, last commit 2025-05-17).

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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Code Implementations(1)

THUKElab/CLEMEOfficial100%
Apache-2.0

The repository of CLEME (EMNLP 2023) and CLEME2.0 (ACL 2025)

122PythonOct 7, 20231 years agoApache-2.0
evaluation-metricsgrammatical-error-correction

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Cite this paper

@article{ye2024cleme,
  title  = {CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction},
  author = {Jingheng Ye and Zishan Xu and Yinghui Li and Linlin Song and Qingyu Zhou and Hai-Tao Zheng and Ying Shen and Wenhao Jiang and Hong-Gee Kim and Ruitong Liu and Xin Su and Zifei Shan},
  year   = {2024},
  eprint = {2407.00934},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.00934v2}
}

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