# FlaCGEC: A Chinese Grammatical Error Correction Dataset with Fine-grained Linguistic Annotation

arXiv: https://arxiv.org/abs/2311.04906 · HTML: https://mlsanity.com/paper/2311.04906/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [GECToR -- Grammatical Error Correction: Tag, Not Rewrite](https://mlsanity.com/paper/2005.12592/) (2020)
- [MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2204.10994/) (2022)
- [Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese Grammatical Error Correction](https://mlsanity.com/paper/2106.01609/) (2021)
- [Do Grammatical Error Correction Models Realize Grammatical Generalization?](https://mlsanity.com/paper/2106.03031/) (2021)
- [Overview of CTC 2021: Chinese Text Correction for Native Speakers](https://mlsanity.com/paper/2208.05681/) (2022)

## Built on by (most cited first, 5 of 5)

- [Large Language Models for Education: A Survey and Outlook](https://mlsanity.com/paper/2403.18105/) (2024)
- [Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends](https://mlsanity.com/paper/2401.07518/) (2024)
- [DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction](https://mlsanity.com/paper/2405.12100/) (2024)
- [Detecting Spelling and Grammatical Anomalies in Russian Poetry Texts](https://mlsanity.com/paper/2505.04507/) (2025)
- [Scoring Edit Impact in Grammatical Error Correction via Embedded Association Graphs](https://mlsanity.com/paper/2604.06573/) (2026)

## Abstract (arXiv)

Chinese Grammatical Error Correction (CGEC) has been attracting growing attention from researchers recently. In spite of the fact that multiple CGEC datasets have been developed to support the research, these datasets lack the ability to provide a deep linguistic topology of grammar errors, which is critical for interpreting and diagnosing CGEC approaches. To address this limitation, we introduce FlaCGEC, which is a new CGEC dataset featured with fine-grained linguistic annotation. Specifically, we collect raw corpus from the linguistic schema defined by Chinese language experts, conduct edits on sentences via rules, and refine generated samples manually, which results in 10k sentences with 78 instantiated grammar points and 3 types of edits. We evaluate various cutting-edge CGEC methods on the proposed FlaCGEC dataset and their unremarkable results indicate that this dataset is challenging in covering a large range of grammatical errors. In addition, we also treat FlaCGEC as a diagnostic dataset for testing generalization skills and conduct a thorough evaluation of existing CGEC models.