Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction
This addresses the problem of limited and non-native training resources for CGEC researchers and practitioners, though it is incremental in data generation.
The authors tackled the lack of high-quality training data and unrealistic test sets in Chinese Grammatical Error Correction (CGEC) by proposing a linguistic rules-based method to generate large-scale training corpora and introducing a benchmark based on native speaker errors. Their approach improved CGEC model performance, as shown in extensive experiments.
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.