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.

本文提出了一种基于语言规则的方法来构建自动生成语法错误的大规模中文语法错误纠正训练语料库，并提出了一个挑战性的中文语法错误纠正基准，旨在解决当前缺乏高质量标注的训练语料库和测试集不是由中文母语人士制作的问题。实验结果表明，我们提出的训练数据构建方法有效地提高了中文语法错误纠正模型的性能，并且我们的基准是进一步发展中文语法错误纠正领域的绝佳资源。