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ACL 2025longmain

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

Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan

ByteDance Inc. · Tsinghua University, Tsinghua University · SUN YAT-SEN UNIVERSITY · Guangming Laboratory · Tsinghua University · WeChat, Tencent

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2025.acl-long.10 ↗

摘要

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.