BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering.

Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model's ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. [ABSTRACT FROM AUTHOR]