Learning to Predict Charges for Criminal Cases with Legal Basis

TL;DR

This paper introduces an attention-based neural network that jointly predicts criminal charges and extracts relevant legal articles, improving accuracy and providing legal basis explanations for case facts.

Contribution

It presents a novel unified model that combines charge prediction with relevant legal article extraction, emphasizing the importance of legal basis in judicial AI systems.

Findings

Legal article extraction improves charge prediction accuracy.

The model effectively handles diverse case expression styles.

Legal basis provision enhances interpretability of predictions.

Abstract

The charge prediction task is to determine appropriate charges for a given case, which is helpful for legal assistant systems where the user input is fact description. We argue that relevant law articles play an important role in this task, and therefore propose an attention-based neural network method to jointly model the charge prediction task and the relevant article extraction task in a unified framework. The experimental results show that, besides providing legal basis, the relevant articles can also clearly improve the charge prediction results, and our full model can effectively predict appropriate charges for cases with different expression styles.