Legal Judgment Prediction Based on Multiclass Information Fusion

Kongfan Zhu,Yujun Li,Rundong Guo,Zeqiang Li,Weifeng Hu,Shirui Pan

doi:10.1155/2020/3089189

Abstract

Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.

Highlights

Legal judgment prediction (LJP) aims to predict the judgment results according to the information based on fact determination, which consists of the fact description, the basic information of defendant, and the court view
We evaluate the performance on three LJP subtasks, including law articles, charges, and
H3 t1, t2, where Hi represents the input of ti and φ is the empty set. is means that the charge prediction depends on law articles, and the terms of penalty prediction depend on both law articles and charges

Summary

Introduction

Legal judgment prediction (LJP) aims to predict the judgment results according to the information based on fact determination, which consists of the fact description, the basic information of defendant, and the court view. LJP techniques can provide inexpensive and useful legal judgment results to people who are unfamiliar with legal terminologies, and they are helpful for the legal consulting. They can serve as a handy reference for professionals (e.g., lawyers and judges), which can improve their work efficiency. Luo et al combined the fact description with the corresponding law articles to predict the charges [4]. Great progress has been made in the LJP, there still exist some problems, such as multiple subtasks, topological dependencies between subtasks, and cases of similar descriptions with different penalties. Yang et al proposed a neural model for the interaction between subtask results [6]

Methods

Results

Conclusion