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.
@article{arxiv.2210.12364,
title = {FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction},
author = {Lvxiaowei Xu and Jianwang Wu and Jiawei Peng and Jiayu Fu and Ming Cai},
journal= {arXiv preprint arXiv:2210.12364},
year = {2023}
}
Comments
Long paper, accepted at the Findings of EMNLP 2022