A Two-Stage Model for Chinese Grammatical Error Correction

Chinese grammatical error correction (GEC) is more challenging than English GEC due to its language characteristics. In this paper, a two-stage model was proposed to solve the Chinese GEC problem. The model consists of two components: a spelling check model and a GEC model. The spelling check model based on language model focuses on correcting spelling errors, while the GEC model based on neural sequence-to-sequence (seq2seq) model focuses on correcting grammatical errors. In addition, two generative methods allow the seq2seq model to correct an erroneous sentence incrementally with repeated inference steps. Furthermore, only one seq2seq model is used for grammatical correction rather than ensemble multiple models, which greatly speeds up the generation of final results and saves computing resources. The two-stage model achieves 31.01 $F_{0.5}$ on NLPCC 2018 test set, significantly outperforms all prior approaches on this task.