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.

介绍一种新的中文语法错误修正数据集（FlaCGEC），该数据集具有细粒度的语言注释，并评估了各种先进的 CGEC 方法在该数据集上的表现，结果显示该数据集具有挑战性，能够涵盖广泛的语法错误，并将其作为诊断数据集进行了评估现有的 CGEC 模型。