Overview of CAIL2018: Legal Judgment Prediction Competition

TL;DR

This paper reviews the CAIL2018 Legal Judgment Prediction competition, highlighting its tasks, participant engagement, and key methods used for predicting legal outcomes based on case facts.

Contribution

It provides a comprehensive overview of the competition's structure, tasks, and results, serving as a reference for future legal AI research and benchmarks.

Findings

High participant engagement with 601 teams and 1,144 contestants.

Detailed analysis of methods and results in legal judgment prediction.

Insights into effective approaches for law article, charge, and prison term prediction.

Abstract

In this paper, we give an overview of the Legal Judgment Prediction (LJP) competition at Chinese AI and Law challenge (CAIL2018). This competition focuses on LJP which aims to predict the judgment results according to the given facts. Specifically, in CAIL2018 , we proposed three subtasks of LJP for the contestants, i.e., predicting relevant law articles, charges and prison terms given the fact descriptions. CAIL2018 has attracted several hundreds participants (601 teams, 1, 144 contestants from 269 organizations). In this paper, we provide a detailed overview of the task definition, related works, outstanding methods and competition results in CAIL2018.