A Legal Judgment Prediction Model Based on BERT, Attention, and Graph Convolutional Network

Legal judgment prediction is one of essential tasks in legal practice. However, most existing models of legal judgment prediction struggle with long documents due to limitations in position encoding and pre-trained language models' capabilities. To address these issues, we propose a hybrid model for legal judgment prediction. The model uses BERT to captures deep semantic information of a legal text, position encoding to preserve its text structure, a graph convolutional network to analyse complex relationships between the entities in the text, and a decoupled attention mechanism to fuse the textual content and positional information. Our experiments show that the proposed model excels in legal judgment prediction tasks, outperforming existing technologies in accuracy and recall, with significant advantages in handling lengthy legal texts.