uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers

TL;DR

uChecker leverages masked pretrained language models with a confusion set-guided masking strategy to perform unsupervised Chinese spelling correction, addressing data scarcity and overfitting issues in low-resource settings.

Contribution

The paper introduces uChecker, a novel unsupervised framework that uses masked language models and confusion set-guided masking for Chinese spelling correction.

Findings

uChecker achieves high accuracy in spelling error detection and correction.

The model outperforms baseline methods on standard datasets.

Confusionset-guided masking improves model performance.

Abstract

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.