mlsanityData to 14 Oct 2026

FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction

arXiv 2210.12364 ↗First posted 22 Oct 2022cs.CLcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 22 Oct 2022.

Builds on

21 of 38 references.

References · 21

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9 more
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Citing papers per year: 2022: 1; 2023: 4; 2024: 3; 2025: 5.

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