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Chinese spelling check is a task to detect and correct spelling mistakes inChinese text. Existing research aims to enhance the text representation and usemulti-source information to improve the detection and correction capabilitiesof models, but does not pay too much attention to improving their ability todistinguish between confusable words. Contrastive learning, whose aim is tominimize the distance in representation space between similar sample pairs, hasrecently become a dominant technique in natural language processing. Inspiredby contrastive learning, we present a novel framework for Chinese spellingchecking, which consists of three modules: language representation, spellingcheck and reverse contrastive learning. Specifically, we propose a reversecontrastive learning strategy, which explicitly forces the model to minimizethe agreement between the similar examples, namely, the phonetically andvisually confusable characters. Experimental results show that our framework ismodel-agnostic and could be combined with existing Chinese spelling checkmodels to yield state-of-the-art performance.

A Chinese Spelling Check Framework Based on Reverse Contrastive Learning