The task of Chinese Spelling Check (CSC) is aiming to detect and correct
spelling errors that can be found in the text. While manually annotating a
high-quality dataset is expensive and time-consuming, thus the scale of the
training dataset is usually very small (e.g., SIGHAN15 only contains 2339
samples for training), therefore supervised-learning based models usually
suffer the data sparsity limitation and over-fitting issue, especially in the
era of big language models. In this paper, we are dedicated to investigating
the \textbf{unsupervised} paradigm to address the CSC problem and we propose a
framework named \textbf{uChecker} to conduct unsupervised spelling error
detection and correction. Masked pretrained language models such as BERT are
introduced as the backbone model considering their powerful language diagnosis
capability. Benefiting from the various and flexible MASKing operations, we
propose a Confusionset-guided masking strategy to fine-train the masked
language model to further improve the performance of unsupervised detection and
correction. Experimental results on standard datasets demonstrate the
effectiveness of our proposed model uChecker in terms of character-level and
sentence-level Accuracy, Precision, Recall, and F1-Measure on tasks of spelling
error detection and correction respectively.

本文介绍了一个名为 uChecker 的框架，该框架采用无监督的方法对汉语拼写错误进行检测和修正，其中使用 BERT 这样的模型作为骨干模型，并采用混淆集引导的 MASKing 策略来优化模型训练。实验结果表明，uChecker 在汉语拼写错误检测和修正任务的字符级和句子级准确性、精度、召回率和 F1 度量方面的表现是有效的。