HD-LJP: A Hierarchical Dependency-based Legal Judgment Prediction Framework for Multi-task Learning

In real -world scenarios, multiple subtasks of legal judgment (such as law article, charge, and term of penalty) have strict task logical order and label topological relation, and their results influence and validate each other. However, most existing methods model them as simple classification problems, which ignores the logical and semantic constraints between subtasks. Besides, they mainly focus on the fact description for judgment results, and ignore the standard legal documents (i.e., the established law articles). To this end, we propose a H ierarchical D ependency -based L egal J udgment P rediction framework (HD-LJP), which integrates task judicial logic, label topological relation, and hierarchical semantics knowledge in legal text effectively. Specifically, HD-LJP employs consistency and distinction distillation to model label topological relation among multiple subtasks, and improve the differentiation of each subtask itself respectively. In addition, for simulating the judicial logic of human judges, we define logical dependencies between subtasks, and then utilize the results of intermediate subtasks to make auxiliary prediction of other subtasks. And, hierarchical semantics knowledge is fully integrated and applied in these two processes, which will profoundly affect the creditability and interpretability of the judgment results. The experimental results show that HD-LJP can improve the prediction performance of three LJP subtasks, especially in the term of penalty. Compared with the existing methods, the macro -F1 on CAIL-small is increased by 13.4%, and 6.9% on CAIL-big. In addition, through further case studies, this paper demonstrates that HD-LJP performs better for tail classes and confusing labels than the current SOTA R -former.