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

uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers