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。