@inproceedings{cao-etal-2024-improving-grammatical,
title = "Improving Grammatical Error Correction by Correction Acceptability Discrimination",
author = "Cao, Bin and
Jiang, Kai and
Pan, Fayu and
Bao, Chenlei and
Fan, Jing",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.772",
pages = "8818--8827",
abstract = "Existing Grammatical Error Correction (GEC) methods often overlook the assessment of sentence-level syntax and semantics in the corrected sentence. This oversight results in final corrections that may not be acceptable in the context of the original sentence. In this paper, to improve the performance of Grammatical Error Correction methods, we propose the post-processing task of Correction Acceptability Discrimination (CAD) which aims to remove invalid corrections by comparing the source sentence and its corrected version from the perspective of {``}sentence-level correctness{''}. To solve the CAD task, we propose a pipeline method where the acceptability of each possible correction combination based on the predicted corrections for a source sentence will be judged by a discriminator. Within the discriminator, we design a symmetrical comparison operator to overcome the conflicting results that might be caused by the sentence concatenation order. Experiments show that our method can averagely improve $F_{0.5}$ score by 1.01{\%} over 13 GEC systems in the BEA-2019 test set.",
}
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%0 Conference Proceedings
%T Improving Grammatical Error Correction by Correction Acceptability Discrimination
%A Cao, Bin
%A Jiang, Kai
%A Pan, Fayu
%A Bao, Chenlei
%A Fan, Jing
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F cao-etal-2024-improving-grammatical
%X Existing Grammatical Error Correction (GEC) methods often overlook the assessment of sentence-level syntax and semantics in the corrected sentence. This oversight results in final corrections that may not be acceptable in the context of the original sentence. In this paper, to improve the performance of Grammatical Error Correction methods, we propose the post-processing task of Correction Acceptability Discrimination (CAD) which aims to remove invalid corrections by comparing the source sentence and its corrected version from the perspective of “sentence-level correctness”. To solve the CAD task, we propose a pipeline method where the acceptability of each possible correction combination based on the predicted corrections for a source sentence will be judged by a discriminator. Within the discriminator, we design a symmetrical comparison operator to overcome the conflicting results that might be caused by the sentence concatenation order. Experiments show that our method can averagely improve F₀.5 score by 1.01% over 13 GEC systems in the BEA-2019 test set.
%U https://aclanthology.org/2024.lrec-main.772
%P 8818-8827
Markdown (Informal)
[Improving Grammatical Error Correction by Correction Acceptability Discrimination](https://aclanthology.org/2024.lrec-main.772) (Cao et al., LREC-COLING 2024)
ACL