uChecker: Masked Pretrained Language Models as Unsupervised Chinese Spelling Checkers. (arXiv:2209.07068v1 [cs.CL])

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 …

arxiv language language models unsupervised