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.- Anthology ID:
- 2022.findings-emnlp.137
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates
- Editors:
- Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1900–1918
- Language:
- URL:
- https://aclanthology.org/2022.findings-emnlp.137
- DOI:
- 10.18653/v1/2022.findings-emnlp.137
- Cite (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.
- Cite (Informal):
- FCGEC: Fine-Grained Corpus for Chinese Grammatical Error Correction (Xu et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/2022.findings-emnlp.137.pdf