@inproceedings{cao-etal-2025-cxggec,
title = "{C}x{GGEC}: Construction-Guided Grammatical Error Correction",
author = "Cao, Yayu and
Wang, Tianxiang and
Xu, Lvxiaowei and
Wang, Zhenyao and
Cai, Ming",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.307/",
doi = "10.18653/v1/2025.acl-long.307",
pages = "6143--6156",
ISBN = "979-8-89176-251-0",
abstract = "The grammatical error correction (GEC) task aims to detect and correct grammatical errors in text to enhance its accuracy and readability. Current GEC methods primarily rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. In this work, we explore the potential of construction grammar (CxG) to improve GEC by leveraging constructions to capture underlying language patterns and guide corrections. We first establish a comprehensive construction inventory from corpora. Next, we introduce a construction prediction model that identifies potential constructions in ungrammatical sentences using a noise-tolerant language model. Finally, we train a CxGGEC model on construction-masked parallel data, which performs GEC by decoding construction tokens into their original forms and correcting erroneous tokens. Extensive experiments on English and Chinese GEC benchmarks demonstrate the effectiveness of our approach."
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<abstract>The grammatical error correction (GEC) task aims to detect and correct grammatical errors in text to enhance its accuracy and readability. Current GEC methods primarily rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. In this work, we explore the potential of construction grammar (CxG) to improve GEC by leveraging constructions to capture underlying language patterns and guide corrections. We first establish a comprehensive construction inventory from corpora. Next, we introduce a construction prediction model that identifies potential constructions in ungrammatical sentences using a noise-tolerant language model. Finally, we train a CxGGEC model on construction-masked parallel data, which performs GEC by decoding construction tokens into their original forms and correcting erroneous tokens. Extensive experiments on English and Chinese GEC benchmarks demonstrate the effectiveness of our approach.</abstract>
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%0 Conference Proceedings
%T CxGGEC: Construction-Guided Grammatical Error Correction
%A Cao, Yayu
%A Wang, Tianxiang
%A Xu, Lvxiaowei
%A Wang, Zhenyao
%A Cai, Ming
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F cao-etal-2025-cxggec
%X The grammatical error correction (GEC) task aims to detect and correct grammatical errors in text to enhance its accuracy and readability. Current GEC methods primarily rely on grammatical labels for syntactic information, often overlooking the inherent usage patterns of language. In this work, we explore the potential of construction grammar (CxG) to improve GEC by leveraging constructions to capture underlying language patterns and guide corrections. We first establish a comprehensive construction inventory from corpora. Next, we introduce a construction prediction model that identifies potential constructions in ungrammatical sentences using a noise-tolerant language model. Finally, we train a CxGGEC model on construction-masked parallel data, which performs GEC by decoding construction tokens into their original forms and correcting erroneous tokens. Extensive experiments on English and Chinese GEC benchmarks demonstrate the effectiveness of our approach.
%R 10.18653/v1/2025.acl-long.307
%U https://aclanthology.org/2025.acl-long.307/
%U https://doi.org/10.18653/v1/2025.acl-long.307
%P 6143-6156
Markdown (Informal)
[CxGGEC: Construction-Guided Grammatical Error Correction](https://aclanthology.org/2025.acl-long.307/) (Cao et al., ACL 2025)
ACL
- Yayu Cao, Tianxiang Wang, Lvxiaowei Xu, Zhenyao Wang, and Ming Cai. 2025. CxGGEC: Construction-Guided Grammatical Error Correction. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6143–6156, Vienna, Austria. Association for Computational Linguistics.