Chinese Grammatical Error Correction (CGEC) aims to generate a correct
sentence from an erroneous sequence, where different kinds of errors are mixed.
This paper divides the CGEC task into two steps, namely spelling error
correction and grammatical error correction. Specifically, we propose a novel
zero-shot approach for spelling error correction, which is simple but
effective, obtaining a high precision to avoid error accumulation of the
pipeline structure. To handle grammatical error correction, we design
part-of-speech (POS) features and semantic class features to enhance the neural
network model, and propose an auxiliary task to predict the POS sequence of the
target sentence. Our proposed framework achieves a 42.11 F0.5 score on CGEC
dataset without using any synthetic data or data augmentation methods, which
outperforms the previous state-of-the-art by a wide margin of 1.30 points.
Moreover, our model produces meaningful POS representations that capture
different POS words and convey reasonable POS transition rules.

该研究提出了一种用于汉语语法错误纠正任务的神经网络模型，包含拼写错误纠正和语法错误纠正两个步骤，并使用词性特征和语义类别特征进行增强以提高模型性能。最终模型在没有使用人工合成数据或数据增强方法的情况下在 CGEC 数据集上取得了 42.11 F0.5 得分，并且在捕捉不同词性词汇和传达合理词性转换规则方面表现突出。