FlaCGEC: A Chinese Grammatical Error Correction Dataset with
  Fine-grained Linguistic Annotation
Hanyue Du Yike Zhao Qingyuan Tian Jiani Wang Lei Wang Yunshi Lan Xuesong Lu
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.