# Legal Judgment Prediction via Multi-Perspective Bi-Feedback Network

## TL;DR

This paper introduces a Multi-Perspective Bi-Feedback Network with Word Collocation Attention for legal judgment prediction, effectively modeling subtask dependencies and word collocations to improve accuracy across multiple prediction tasks.

## Contribution

The paper proposes a novel network architecture that leverages multi-perspective feedback and word collocation attention to enhance legal judgment prediction accuracy.

## Key findings

- Significant improvements over baselines on all tasks
- Effective modeling of subtask dependencies
- Enhanced handling of cases with similar descriptions

## Abstract

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.