JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction

• URL: http://arxiv.org/abs/2511.21700v1
• Date: Sun, 16 Nov 2025 05:58:39 GMT
• Title: JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction
• Authors: Yuhao Zhan, Yuqing Zhang, Jing Yuan, Qixiang Ma, Zhiqi Yang, Yu Gu, Zemin Liu, Fei Wu,
• Abstract summary: We introduce the Judge of Edit-Level Validity (JELV) to validate correction edits from grammaticality, faithfulness, and fluency.<n>Using our proposed human-annotated Pair-wise Edit-level Validity dataset (PEVData) as benchmark, JELV offers two implementations.<n>We apply JELV to filter LLM-generated correction candidates, expanding the BEA19's single-reference dataset containing 38,692 source sentences.
• Score: 22.662896396339107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Existing Grammatical Error Correction (GEC) systems suffer from limited reference diversity, leading to underestimated evaluation and restricted model generalization. To address this issue, we introduce the Judge of Edit-Level Validity (JELV), an automated framework to validate correction edits from grammaticality, faithfulness, and fluency. Using our proposed human-annotated Pair-wise Edit-level Validity Dataset (PEVData) as benchmark, JELV offers two implementations: a multi-turn LLM-as-Judges pipeline achieving 90% agreement with human annotators, and a distilled DeBERTa classifier with 85% precision on valid edits. We then apply JELV to reclassify misjudged false positives in evaluation and derive a comprehensive evaluation metric by integrating false positive decoupling and fluency scoring, resulting in state-of-the-art correlation with human judgments. We also apply JELV to filter LLM-generated correction candidates, expanding the BEA19's single-reference dataset containing 38,692 source sentences. Retraining top GEC systems on this expanded dataset yields measurable performance gains. JELV provides a scalable solution for enhancing reference diversity and strengthening both evaluation and model generalization.

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