Chinese spelling check is a task to detect and correct spelling mistakes in
Chinese text. Existing research aims to enhance the text representation and use
multi-source information to improve the detection and correction capabilities
of models, but does not pay too much attention to improving their ability to
distinguish between confusable words. Contrastive learning, whose aim is to
minimize the distance in representation space between similar sample pairs, has
recently become a dominant technique in natural language processing. Inspired
by contrastive learning, we present a novel framework for Chinese spelling
checking, which consists of three modules: language representation, spelling
check and reverse contrastive learning. Specifically, we propose a reverse
contrastive learning strategy, which explicitly forces the model to minimize
the agreement between the similar examples, namely, the phonetically and
visually confusable characters. Experimental results show that our framework is
model-agnostic and could be combined with existing Chinese spelling check
models to yield state-of-the-art performance.

通过提出反向对比学习策略及引入三个模块：语言表示、拼写检查和反向对比学习，构建了一种新的中文拼写检查框架。实验证明，此框架可与现有模型结合，实现最先进的性能。