  Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task
and a common application in human daily life. Recently, many data-driven
approaches are proposed for the development of CGEC research. However, there
are two major limitations in the CGEC field: First, the lack of high-quality
annotated training corpora prevents the performance of existing CGEC models
from being significantly improved. Second, the grammatical errors in widely
used test sets are not made by native Chinese speakers, resulting in a
significant gap between the CGEC models and the real application. In this
paper, we propose a linguistic rules-based approach to construct large-scale
CGEC training corpora with automatically generated grammatical errors.
Additionally, we present a challenging CGEC benchmark derived entirely from
errors made by native Chinese speakers in real-world scenarios. Extensive
experiments and detailed analyses not only demonstrate that the training data
constructed by our method effectively improves the performance of CGEC models,
but also reflect that our benchmark is an excellent resource for further
development of the CGEC field.
