BCA: Bilinear Convolutional Neural Networks and Attention Networks for legal question answering

Abstract

The National Judicial Examination of China is an essential examination for selecting legal practitioners. In recent years, people have tried to use machine learning algorithms to answer examination questions. With the proposal of JEC-QA (Zhong et al. 2020), the judicial examination becomes a particular legal task. The data of judicial examination contains two types, i.e., Knowledge-Driven questions and Case-Analysis questions. Both require complex reasoning and text comprehension, thus challenging computers to answer judicial examination questions. We propose Bilinear Convolutional Neural Networks and Attention Networks (BCA) in this paper, which is an improved version based on the model proposed by our team on the Challenge of AI in Law 2021 judicial examination task. It has two essential modules, Knowledge-Driven Module (KDM) for local features extraction and Case-Analysis Module (CAM) for the semantic difference clarification between the question stem and the options. We also add a post-processing module to correct the results in the final stage. The experimental results show that our system achieves state-of-the-art in the offline test of the judicial examination task.