arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1807.02478cs.CL
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CAIL2018:一个用于判决预测的大规模法律数据集

CAIL2018: A Large-Scale Legal Dataset for Judgment Prediction

Tsinghua University(清华大学);Peking University(北京大学);Chinese Academy of Sciences(中国科学院);China Justice Big Data Institute(中国司法大数据研究院);Supreme People’s Court(最高人民法院)

Chaojun Xiao, Haoxi Zhong, Zhipeng Guo, Cunchao Tu, Zhiyuan Liu, Maosong Sun, Yansong Feng, Xianpei Han, Zhen Hu, Heng Wang, Jianfeng Xu

摘要:

在本文中,我们介绍了\textbf{C}中文\textbf{AI}和\textbf{L}法律挑战数据集(CAIL 2018),这是第一个用于判决预测的大规模中文法律数据集。\dataset包含中国最高人民法院发布的超过$2.6$万个刑事案件,是现有判决预测工作中其他数据集的数倍。而且,判决结果的注释更加详细、丰富。它包括适用的法律条款、罪名和刑期,这些都是根据案件的事实描述来推断的。为了比较,我们实现了几个传统的文本分类基线的判决预测和实验结果表明,它仍然是一个挑战,目前的模型预测的判决结果的法律案件,特别是在监狱的刑期。为了帮助研究人员改进法律判断预测,\dataset和基线都将在CAIL竞赛\footnote{http://cail.cipsc.org.cn/}后发布。

英文摘要:

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/}.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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