JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction
Yuhao Zhan, Yuqing Zhang, Jing Yuan, Qixiang Ma, Zhiqi Yang, Yu Gu, Zemin Liu, Fei Wu
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
BibTeX
@inproceedings{aaai2026_jelvajudgeofedit,
title = {JELV: A Judge of Edit-Level Validity for Evaluation and Automated Reference Expansion in Grammatical Error Correction},
author = {Yuhao Zhan and Yuqing Zhang and Jing Yuan and Qixiang Ma and Zhiqi Yang and Yu Gu and Zemin Liu and Fei Wu},
booktitle = {AAAI 2026},
year = {2026}
}