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.

本文概述了 CAIL2018 的法律判决预测比赛，该比赛旨在根据给定的事实描述预测判决结果。在比赛中，我们为参赛者提供了三个子任务，即预测相关法律条款、罪名和刑期。CAIL2018 吸引了数百个参赛者，本文提供了任务定义、相关工作、优秀方法和比赛结果的详细概述。

CAIL2018: 法律判决预测竞赛概述

Overview of CAIL2018: Legal Judgment Prediction Competition

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{this http URL}.

本文介绍了 CAIL2018 挑战数据集，以及其中包含的中国法律判例数据，分析了现有文本分类方法在预测判决结果方面的挑战，特别是在刑期上的预测还有待改进。