Overview of CAIL2018: Legal Judgment Prediction Competition

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

Overview of CAIL2018: Legal Judgment Prediction Competition

Artificial Intelligence 2018-10-16 v1

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

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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.

Keywords

legal judgment prediction CAIL2018 law article prediction charge prediction prison term prediction competition overview

Cite

@article{arxiv.1810.05851,
  title  = {Overview of CAIL2018: Legal Judgment Prediction Competition},
  author = {Haoxi Zhong and Chaojun Xiao and Zhipeng Guo and Cunchao Tu and Zhiyuan Liu and Maosong Sun and Yansong Feng and Xianpei Han and Zhen Hu and Heng Wang and Jianfeng Xu},
  journal= {arXiv preprint arXiv:1810.05851},
  year   = {2018}
}

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6 pages, 1 table

Overview of CAIL2018: Legal Judgment Prediction Competition

Abstract

Keywords

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