CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

TL;DR

This paper introduces CAIL2018, a large-scale Chinese legal dataset with detailed annotations for judgment prediction, highlighting the challenge for current models to accurately predict legal outcomes, especially prison terms.

Contribution

It provides the first large-scale Chinese legal dataset with rich annotations, enabling better research and benchmarking in legal judgment prediction.

Findings

Current models struggle with prison term prediction.

The dataset is significantly larger than existing datasets.

Baseline models show room for improvement in legal judgment prediction.

Abstract

In this paper, we introduce the \textbf{C}hinese \textbf{AI} and \textbf{L}aw challenge dataset (CAIL2018), the first large-scale Chinese legal dataset for judgment prediction. \dataset contains more than $2.6$ million criminal cases published by the Supreme People's Court of China, which are several times larger than other datasets in existing works on judgment prediction. Moreover, the annotations of judgment results are more detailed and rich. It consists of applicable law articles, charges, and prison terms, which are expected to be inferred according to the fact descriptions of cases. For comparison, we implement several conventional text classification baselines for judgment prediction and experimental results show that it is still a challenge for current models to predict the judgment results of legal cases, especially on prison terms. To help the researchers make improvements on legal judgment prediction, both \dataset and baselines will be released after the CAIL competition\footnote{http://cail.cipsc.org.cn/}.