EK-CPSG: Enhancing Confusing Charge Prediction with Criminal Charge Definition

The task of charge prediction aims to determine the ultimate charge of defendant by analyzing factual description in legal cases. Existing approaches mainly use the factual descriptions provided in the dataset to guide this task. However, Criminal Charge Definitions (CCDs) in laws and regulations is a rich additional information that can effectively distinguish between confusing charges, which is not considered by existing models. In this work, we present a model termed Charge Prediction Sequence Graph with External Knowledge (EK-CPSG), which integrates information about Criminal Charge Definitions (CCDs). This method is based on the charge prediction model CP-KG, which overcomes the problem of inaccurate charge prediction due to unbalanced data and incomplete extraction of key elements. Firstly, we use attention mechanism to effectively combine CCDs encoded by TextCNN with case facts encoded by Bi-GRU. Secondly, we focus on the most relevant elements of case facts and CCDs. Thirdly, to evaluate EK-CPSG, we constructed CCs-8 from CAIL2018 dataset, which includes 8 confusing charges, and conducted a large number of experiments on it. The experimental results indicate that EK-CPSG achieves scores of 90.96% in Macro-F1. Compared to the baseline model TFIDF-SVM, EK-CPSG shows significant improvements with 14.55%. When compared to CP-KG, there is an approximately 0.73% enhancement. Finally, through ablation experiments, we confirmed the rationality and reliability of the introduction of CCDs.