Learning to Predict Charges for Criminal Cases with Legal Basis
This work addresses the need for more accurate legal assistant systems by enhancing charge prediction with legal basis, though it is incremental as it builds on existing methods by integrating article extraction.
The authors tackled the problem of predicting appropriate criminal charges from case descriptions by incorporating relevant law articles, resulting in improved charge prediction accuracy through a unified attention-based neural network model.
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.