Legal Judgment Prediction via Fine-Grained Element Graphs and External Knowledge

Legal Judgment Prediction (LJP), a paramount application of artificial intelligence in law, aims to infer outcomes from fact descriptions. These outcomes mainly involve determining applicable law articles, charges, and the term of penalty. Existing approaches typically treat this task as a text classification problem, utilizing end-to-end black box models for automatic prediction. However, these approaches overlook the fine-grained elements within cases and limit the model’s interpretability. In light of these limitations, we propose a novel approach for LJP. Our method employs Fine-Grained Element Graphs (FEGs) to represent fact descriptions and applies Graph Attention Network (GAT) to learn graph representation for subsequent prediction tasks. FEG is characterized by capturing the relevant adjudicative elements of each fact description and preserving important relations among them, such as causality and temporal sequences while filtering out irrelevant content. It enhances LJP task performance and provides interpretable references for legal professionals. Since increased or decreased penalties corresponding to different elements cannot be obtained from fact descriptions, we introduce Sentencing Guidance (SG) and combine it with FEGs as a supplementary feature. Experiments confirm the effectiveness of our method. Specifically, compared to the best baseline model, it improves macro-F1 by 3.09%, 0.57%, and 1.78% for the subtasks of law article prediction, charge prediction, and term of penalty prediction.