Bridging the Gap: A Self-Learning Model Using Implicit Knowledge for Chinese Spelling Correction

Chinese Spelling Correction (CSC) is a challenging and essential task in natural language processing. In this study, we introduces a new method for Chinese Spelling Correction (CSC) that addresses three unattended areas in prior studies. Firstly, we use an Implicit Knowledge Extraction Network to overcome limitations of conventional methods that rely on explicit knowledge alone. Secondly, we use KL divergence to limit the effect of incorrect characters on semantic understanding, ensuring consistent meaning. Finally, we employ a Cor-Det framework rather than the traditional Det-Cor framework, offering more consistent learning objectives. Tests on three SIGHAN benchmarks show this method significantly surpassing baseline models, highlighting the crucial role of implicit knowledge in Chinese Spelling Correction tasks.