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    <abstract>Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently. However, it is still immature in Chinese GEC due to limited high-quality data from native speakers in terms of category and scale. In this paper, we present FCGEC, a fine-grained corpus to detect, identify and correct the grammatical errors. FCGEC is a human-annotated corpus with multiple references, consisting of 41,340 sentences collected mainly from multi-choice questions in public school Chinese examinations. Furthermore, we propose a Switch-Tagger-Generator (STG) baseline model to correct the grammatical errors in low-resource settings. Compared to other GEC benchmark models, experimental results illustrate that STG outperforms them on our FCGEC. However, there exists a significant gap between benchmark models and humans that encourages future models to bridge it.</abstract>
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%0 Conference Proceedings
%T FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
%A Xu, Lvxiaowei
%A Wu, Jianwang
%A Peng, Jiawei
%A Fu, Jiayu
%A Cai, Ming
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F xu-etal-2022-fcgec
%X Grammatical Error Correction (GEC) has been broadly applied in automatic correction and proofreading system recently. However, it is still immature in Chinese GEC due to limited high-quality data from native speakers in terms of category and scale. In this paper, we present FCGEC, a fine-grained corpus to detect, identify and correct the grammatical errors. FCGEC is a human-annotated corpus with multiple references, consisting of 41,340 sentences collected mainly from multi-choice questions in public school Chinese examinations. Furthermore, we propose a Switch-Tagger-Generator (STG) baseline model to correct the grammatical errors in low-resource settings. Compared to other GEC benchmark models, experimental results illustrate that STG outperforms them on our FCGEC. However, there exists a significant gap between benchmark models and humans that encourages future models to bridge it.
%R 10.18653/v1/2022.findings-emnlp.137
%U https://aclanthology.org/2022.findings-emnlp.137
%U https://doi.org/10.18653/v1/2022.findings-emnlp.137
%P 1900-1918

Markdown (Informal)

[FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction](https://aclanthology.org/2022.findings-emnlp.137) (Xu et al., Findings 2022)

• FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (Xu et al., Findings 2022)

ACL

• Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, and Ming Cai. 2022. FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 1900–1918, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.