@inproceedings{sakai-etal-2025-impara,
title = "{IMPARA}-{GED}: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator",
author = "Sakai, Yusuke and
Goto, Takumi and
Watanabe, Taro",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.1315/",
doi = "10.18653/v1/2025.findings-acl.1315",
pages = "25647--25654",
ISBN = "979-8-89176-256-5",
abstract = "We propose IMPARA-GED, a novel reference-free automatic grammatical error correction (GEC) evaluation method with grammatical error detection (GED) capabilities. We focus on the quality estimator of IMPARA, an existing automatic GEC evaluation method, and construct that of IMPARA-GED using a pre-trained language model with enhanced GED capabilities. Experimental results on SEEDA, a meta-evaluation dataset for automatic GEC evaluation methods, demonstrate that IMPARA-GED achieves the highest correlation with human sentence-level evaluations."
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%0 Conference Proceedings
%T IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator
%A Sakai, Yusuke
%A Goto, Takumi
%A Watanabe, Taro
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F sakai-etal-2025-impara
%X We propose IMPARA-GED, a novel reference-free automatic grammatical error correction (GEC) evaluation method with grammatical error detection (GED) capabilities. We focus on the quality estimator of IMPARA, an existing automatic GEC evaluation method, and construct that of IMPARA-GED using a pre-trained language model with enhanced GED capabilities. Experimental results on SEEDA, a meta-evaluation dataset for automatic GEC evaluation methods, demonstrate that IMPARA-GED achieves the highest correlation with human sentence-level evaluations.
%R 10.18653/v1/2025.findings-acl.1315
%U https://aclanthology.org/2025.findings-acl.1315/
%U https://doi.org/10.18653/v1/2025.findings-acl.1315
%P 25647-25654
Markdown (Informal)
[IMPARA-GED: Grammatical Error Detection is Boosting Reference-free Grammatical Error Quality Estimator](https://aclanthology.org/2025.findings-acl.1315/) (Sakai et al., Findings 2025)
ACL