Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

TL;DR

This paper introduces a linguistic rules-based method to generate high-quality Chinese grammatical error correction data and creates a benchmark from native speaker errors, advancing CGEC research.

Contribution

It presents a novel rules-based approach for constructing large-scale training data and a real-world benchmark from native speaker errors, addressing key limitations in CGEC.

Findings

Generated training data improves CGEC model performance

Benchmark reflects errors made by native Chinese speakers

Method enhances the realism and quality of CGEC datasets

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.