@inproceedings{cao-etal-2024-improving,
    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.",
}

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.
Cao, Bin Jiang, Kai Pan, Fayu Bao, Chenlei Fan, Jing
Improving Grammatical Error Correction by Correction Acceptability Discrimination