Enhancing Legal Judgment Prediction with Attentional Networks Utilizing Legal Event Types

Yaoyao Yu, Yihui Qiu

Abstract: Legal Judgment Prediction (LJP) is a critical task that aims to predict charges, articles, and terms of penalty based on the fact descriptions provided in the cases. However, current LJP methods often fail to fully utilize the important aspect of legal event information, leading to suboptimal predictions. To address this issue, our proposed model introduces a legal event type attention mechanism, which effectively identifies key event information within the fact descriptions. By combining event-aware and event-free representations, our framework enables a comprehensive understanding of the fact descriptions, leading to better performance on LJP. Importantly, our approach outperforms the state-of-the-art models, achieving an average improvement of 3.86% in the prediction of articles, 1.82% in the prediction of charges, and 5.24% in the prediction of terms of penalty.