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Judgment prediction for legal cases has attracted much research efforts forits practice use, of which the ultimate goal is prison term prediction. Whileexisting work merely predicts the total prison term, in reality a defendant isoften charged with multiple crimes. In this paper, we argue that charge-basedprison term prediction (CPTP) not only better fits realistic needs, but alsomakes the total prison term prediction more accurate and interpretable. Wecollect the first large-scale structured data for CPTP and evaluate severalcompetitive baselines. Based on the observation that fine-grained featureselection is the key to achieving good performance, we propose the Deep GatingNetwork (DGN) for charge-specific feature selection and aggregation.Experiments show that DGN achieves the state-of-the-art performance.

Charge-Based Prison Term Prediction with Deep Gating Network