Focus Is What You Need For Chinese Grammatical Error Correction
This work addresses a specific issue in natural language processing for Chinese text, offering an incremental improvement in training methods for CGEC models.
The paper tackles the problem of Chinese Grammatical Error Correction (CGEC) by showing that using multiple references in training does not improve model performance, and instead proposes a training strategy called OneTarget to enhance model focus, leading to improved CGEC results.
Chinese Grammatical Error Correction (CGEC) aims to automatically detect and correct grammatical errors contained in Chinese text. In the long term, researchers regard CGEC as a task with a certain degree of uncertainty, that is, an ungrammatical sentence may often have multiple references. However, we argue that even though this is a very reasonable hypothesis, it is too harsh for the intelligence of the mainstream models in this era. In this paper, we first discover that multiple references do not actually bring positive gains to model training. On the contrary, it is beneficial to the CGEC model if the model can pay attention to small but essential data during the training process. Furthermore, we propose a simple yet effective training strategy called OneTarget to improve the focus ability of the CGEC models and thus improve the CGEC performance. Extensive experiments and detailed analyses demonstrate the correctness of our discovery and the effectiveness of our proposed method.