Legal Judgment Prediction via Relational Learning

Given a legal case and all law articles, L egal J udgment P rediction (LJP) is to predict the case's violated articles, charges and term of penalty. Naturally, these labels are entangled among different tasks and within a task. For example, each charge is only logically or semantically related to some fixed articles. Ignoring these constraints, LJP methods would predict unreliable results. To solve this problem, we first formalize LJP as a node classification problem over a global consistency graph derived from the training set. In terms of node encoder, we utilize a masked transformer network to obtain case aware node representations consistent among tasks and discriminative within a task. In terms of node classifier, each node's label distribution is dependent on its neighbors' in this graph to achieve local consistency by relational learning. Both the node encoder and classifier are optimized by variational EM. Finally, we propose a novel measure to evaluate self-consistency of classification results. Experimental results on two benchmark datasets demonstrate that the F1 improvement of our method is about 4. 8% compared with SOTA methods.