FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction
Lvxiaowei Xu, Jianwang Wu, Jiawei Peng, Jiayu Fu, Ming Cai · 2022
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.Our annotation corpus and codes are available at https://github.com/xlxwalex/FCGEC† .