Knowledge-Enriched Multi-Cross Attention Network for Legal Judgment Prediction - Congqing He; Tien-Ping Tan; Xiaobo Zhang; Sheng Xue (IEEE Access, 2023)
Legal judgment prediction (LJP) automatically predicts the judgment results of a legal case based on its fact description, which has excellent prospects in judicial assistance systems and consultation services for the public. Most previous studies either focused on enhancing LJP’s performance while ignoring the issue of confusing charges and law articles, or only used law articles to improve the judgment of confusing verdicts, resulting in the limited model performance. This paper introduces legal charge knowledge as a type of knowledge to enhance the representation of fact descriptions and incorporates it into deep neural networks. We then propose a Knowledge-enriched Multi-Cross Attention Network (KEMCAN) to improve LJP’s performance, and resolve legal cases involving confusing charges and law articles. Specifically, a cross-attention mechanism is proposed to model the relationship between legal charge knowledge and fact description in a unified model. The experimental results demonstrate that our model outperforms the state-of-the-art methods on two real-world datasets, achieving an average improvement of 3.95% in macro-F1 for charge prediction and 1.98% for law article prediction.