The Legal Judgment Prediction (LJP) is to determine judgment results based on
the fact descriptions of the cases. LJP usually consists of multiple subtasks,
such as applicable law articles prediction, charges prediction, and the term of
the penalty prediction. These multiple subtasks have topological dependencies,
the results of which affect and verify each other. However, existing methods
use dependencies of results among multiple subtasks inefficiently. Moreover,
for cases with similar descriptions but different penalties, current methods
cannot predict accurately because the word collocation information is ignored.
In this paper, we propose a Multi-Perspective Bi-Feedback Network with the Word
Collocation Attention mechanism based on the topology structure among subtasks.
Specifically, we design a multi-perspective forward prediction and backward
verification framework to utilize result dependencies among multiple subtasks
effectively. To distinguish cases with similar descriptions but different
penalties, we integrate word collocations features of fact descriptions into
the network via an attention mechanism. The experimental results show our model
achieves significant improvements over baselines on all prediction tasks.

本文提出了一种基于多角度双向反馈网络和词语搭配注意力机制的法律判决预测方法，以拓扑结构作为多个子任务的依赖关系，通过充分利用它们之间的结果依赖关系来解决现有方法在多个子任务之间的效率低下和相似描述但不同刑罚的问题，并在所有预测任务上显著提高了模型效果。