Linguistic Rules-Based Corpus Generation for Native C hinese Grammatical Error Correction (2022)

Abstract: 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.

## introduction in the field of theoretical linguistics, the native speaker is the authority of the grammar. ## conclusion in this paper, we propose the clg based on linguistic rules to automatically generate ungrammatical 583 sentences from correct texts for obtaining largescale cgec training corpus. besides, we collect sentences with grammatical errors written by chinese native speakers and construct a more challenging cgec benchmark nacgec. experimental results and detailed analyses indicate that nacgec is consistent with the language habits of chinese native speakers, and the training data generated by clg effectively improves the model performance on nacgec and other mainstream benchmarks. we hope our work provides better resources and a new direction for future cgec research.