  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.
