Although existing neural network approaches have achieved great success on
Chinese spelling correction, there is still room to improve. The model is
required to avoid over-correction and to distinguish a correct token from its
phonological and visually similar ones. In this paper, we propose an
error-guided correction model (EGCM) to improve Chinese spelling correction. By
borrowing the powerful ability of BERT, we propose a novel zero-shot error
detection method to do a preliminary detection, which guides our model to
attend more on the probably wrong tokens in encoding and to avoid modifying the
correct tokens in generating. Furthermore, we introduce a new loss function to
integrate the error confusion set, which enables our model to distinguish
easily misused tokens. Moreover, our model supports highly parallel decoding to
meet real application requirements. Experiments are conducted on widely used
benchmarks. Our model achieves superior performance against state-of-the-art
approaches by a remarkable margin, on both the correction quality and
computation speed.

本文提出了一种基于神经网络和 BERT 的错误指导拼音检查模型，支持高度并行化解码，通过引入新的损失函数融合错词集，提高了错词的辨别能力和纠错质量。研究证明，在基准测试上，该模型在纠错质量和计算速度方面均显著优于现有技术。