Match and Retrieval: Legal Similar Case Retrieval via Graph Matching Network

Legal document retrieval is a crucial application in the field of legal Artificial Intelligence (AI). It involves retrieving the most relevant legal cases from a legal document database when inputting a query. Mainstream solutions typically employ pre-trained language models or large language models for retrieval. However, both of these methods rely on black-box models trained on unlabeled data, resulting in a lack of interpretability in the model's retrieval outcomes. Graph Neural Networks (GNNs) possess strong representational and interpretable capabilities. Therefore, representing legal cases using graph-structured data and performing the Legal Similar Case Retrieval (LSCR) task through GNNs is a promising approach. Nevertheless, typical GNNs primarily focus on node information within graph-structured data and overlook edge and inter-graph interaction information, leading to lower retrieval accuracy. To address these issues, in this paper, we propose a Legal case retrieval model utilizing a Graph Matching Network, called Legal-GMN. We evaluate the model on a dataset composed of real legal judgment cases. Experimental results demonstrate that Legal-GMN effectively enhances the retrieval accuracy for the LSCR task. Compared with baseline methods, Legal-GMN improves retrieval precision by approximately 15% and enhances retrieval efficiency by approximately 90%. Remarkably, while maintaining State-Of-The-Art (SOTA) performance, Legal-GMN can also generate visualized graphs of the retrieval process, significantly enhancing its interpretability.