  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.
