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.

翻译：尽管现有的神经网络方法在中文拼写纠错方面取得了显著成功，但仍存在改进空间。模型需避免过度纠错，并区分正确字符与其音近、形近字符。本文提出一种基于错误指导的纠错模型（EGCM）以提升中文拼写纠错性能。通过借鉴BERT的强大能力，我们设计了一种新颖的零样本错误检测方法进行初步检测，引导模型在编码时更关注可能错误的字词，并在生成时避免修改正确字词。此外，我们引入一种融合错误混淆集的新型损失函数，使模型能够区分易误用的字词。同时，模型支持高度并行的解码方式以满足实际应用需求。在广泛使用的基准数据集上的实验表明，我们的模型在纠错质量和计算速度上均显著超越现有最优方法。