  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.
