A Legal Multi-Choice Question Answering Model Based on DeBERTa and Attention Mechanism

This paper presents a novel multi-choice question answering model tailored for the legal domain, which integrates a DeBERTa-based framework with bilinear attention mechanisms. The model is designed to enhance the interpretation of complex legal texts, utilising vector similarity searches to align questions with pertinent statutes and a neural network to analyse this data effectively. It aims to improve the accuracy of legal question answering by employing advanced techniques of natural language processing. A binary classifier is also incorporated to assess answer options and generate conclusive responses. Experimental results demonstrate the model's superiority over traditional methods, particularly in handling intricate legal questions. This study underscores the model's potential to advance legal AI technology significantly, offering substantial benefits for judicial examinations and legal professionals.