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Although existing neural network approaches have achieved great success onChinese spelling correction, there is still room to improve. The model isrequired to avoid over-correction and to distinguish a correct token from itsphonological and visually similar ones. In this paper, we propose anerror-guided correction model (EGCM) to improve Chinese spelling correction. Byborrowing the powerful ability of BERT, we propose a novel zero-shot errordetection method to do a preliminary detection, which guides our model toattend more on the probably wrong tokens in encoding and to avoid modifying thecorrect tokens in generating. Furthermore, we introduce a new loss function tointegrate the error confusion set, which enables our model to distinguisheasily misused tokens. Moreover, our model supports highly parallel decoding tomeet real application requirements. Experiments are conducted on widely usedbenchmarks. Our model achieves superior performance against state-of-the-artapproaches by a remarkable margin, on both the correction quality andcomputation speed.

An Error-Guided Correction Model for Chinese Spelling Error Correction