A BERT-Based Model for Legal Document Proofreading

The chapter introduces a BERT-based model for legal document proofreading, addressing the critical need for accuracy and precision in legal texts. The model integrates two structurally identical MLMs with varying training methods to enhance performance. It includes a grammar check module and a spelling check module, complementing each other to correct errors effectively. The spelling check module employs a Limiter algorithm to balance precision and recall by considering pinyin similarity. The model is trained on a million artificially generated legal document sentences, demonstrating superior performance compared to baseline models and large language models. The chapter also discusses the model's architecture, training process, and experimental evaluation, highlighting its potential to revolutionize legal document proofreading.