Charge-Based Prison Term Prediction with Deep Gating Network
This addresses a practical need in legal judgment prediction for more accurate and interpretable sentencing, though it is incremental by building on existing work with a new dataset and method.
The paper tackles the problem of predicting prison terms for defendants charged with multiple crimes by introducing charge-based prison term prediction (CPTP) and proposes the Deep Gating Network (DGN) for feature selection, achieving state-of-the-art performance.
Judgment prediction for legal cases has attracted much research efforts for its practice use, of which the ultimate goal is prison term prediction. While existing work merely predicts the total prison term, in reality a defendant is often charged with multiple crimes. In this paper, we argue that charge-based prison term prediction (CPTP) not only better fits realistic needs, but also makes the total prison term prediction more accurate and interpretable. We collect the first large-scale structured data for CPTP and evaluate several competitive baselines. Based on the observation that fine-grained feature selection is the key to achieving good performance, we propose the Deep Gating Network (DGN) for charge-specific feature selection and aggregation. Experiments show that DGN achieves the state-of-the-art performance.