Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

10-2023

Abstract

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.

Keywords

Chinese Grammatical Error Correction, Deep Learning, Fine-grained Linguistic Annotation

Discipline

Asian Studies | Databases and Information Systems | East Asian Languages and Societies

Publication

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, Birmingham, October 21-25

First Page

5321

Last Page

5325

ISBN

9798400701245

Identifier

10.1145/3583780.3615119

Publisher

ACM

City or Country

New York

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1145/3583780.3615119

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