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    <abstract>Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. However, mainstream evaluation metrics, i.e., reference-based metrics, introduce bias into the multi-reference evaluation by extracting edits without considering the presence of multiple references. To overcome this issue, we propose Chunk-LE Multi-reference Evaluation (CLEME), designed to evaluate GEC systems in the multi-reference evaluation setting. CLEME builds chunk sequences with consistent boundaries for the source, the hypothesis and references, thus eliminating the bias caused by inconsistent edit boundaries. Furthermore, we observe the consistent boundary could also act as the boundary of grammatical errors, based on which the F₀.5 score is then computed following the correction independence assumption. We conduct experiments on six English reference sets based on the CoNLL-2014 shared task. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of CLEME. Further analysis reveals that CLEME is robust to evaluate GEC systems across reference sets with varying numbers of references and annotation styles. All the source codes of CLEME are released at https://github.com/THUKElab/CLEME.</abstract>
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%0 Conference Proceedings
%T CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction
%A Ye, Jingheng
%A Li, Yinghui
%A Zhou, Qingyu
%A Li, Yangning
%A Ma, Shirong
%A Zheng, Hai-Tao
%A Shen, Ying
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ye-etal-2023-cleme
%X Evaluating the performance of Grammatical Error Correction (GEC) systems is a challenging task due to its subjectivity. Designing an evaluation metric that is as objective as possible is crucial to the development of GEC task. However, mainstream evaluation metrics, i.e., reference-based metrics, introduce bias into the multi-reference evaluation by extracting edits without considering the presence of multiple references. To overcome this issue, we propose Chunk-LE Multi-reference Evaluation (CLEME), designed to evaluate GEC systems in the multi-reference evaluation setting. CLEME builds chunk sequences with consistent boundaries for the source, the hypothesis and references, thus eliminating the bias caused by inconsistent edit boundaries. Furthermore, we observe the consistent boundary could also act as the boundary of grammatical errors, based on which the F₀.5 score is then computed following the correction independence assumption. We conduct experiments on six English reference sets based on the CoNLL-2014 shared task. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of CLEME. Further analysis reveals that CLEME is robust to evaluate GEC systems across reference sets with varying numbers of references and annotation styles. All the source codes of CLEME are released at https://github.com/THUKElab/CLEME.
%R 10.18653/v1/2023.emnlp-main.378
%U https://aclanthology.org/2023.emnlp-main.378
%U https://doi.org/10.18653/v1/2023.emnlp-main.378
%P 6174-6189

Markdown (Informal)

[CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction](https://aclanthology.org/2023.emnlp-main.378) (Ye et al., EMNLP 2023)

• CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction (Ye et al., EMNLP 2023)

ACL

• Jingheng Ye, Yinghui Li, Qingyu Zhou, Yangning Li, Shirong Ma, Hai-Tao Zheng, and Ying Shen. 2023. CLEME: Debiasing Multi-reference Evaluation for Grammatical Error Correction. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 6174–6189, Singapore. Association for Computational Linguistics.