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正方面取得了很大成功,但仍有改进的空间。该模型要避免过度纠正,并区分正确的词元与其音韵上和视觉上相似的词元。本文提出一种错误引导校正模型 (EGCM) 来改进中文拼写纠正。我们借鉴 BERT 的强大能力,提出了一种新颖的零-shot 错误检测方法,以进行初步检测,指导我们的模型在编码时更多地关注可能错误的词元,而在生成时避免修改正确的词元。此外,我们引入了一种新的损失函数,集成了错误混淆集,使我们的模型能够区分容易误用的词元。此外,我们的模型支持高度并行的解码,以满足实际应用需求。实验在广泛使用的基准测试中进行。我们的模型在纠正质量和计算速度上都比现有最先进的方法具有显著的优势。