Automatic Judgment Prediction via Legal Reading Comprehension

• 作 者: Shangbang Long Cunchao Tu Zhiyuan Liu Maosong Sun
• 机 构: Department of Machine Learning,Carnegie Mellon University,Pittsburgh,USA
• 出 处: 第十八届中国计算语言学大会暨中国中文信息学会2019学术年会
• 发表日期: 2019年8期

　　Automatic judgment prediction aims to predict the judicial results based on case materials.It has been studied for several decades mainly by lawyers and judges,considered as a novel and prospective application of artificial intelligence techniques in the legal field.Most existing methods follow the text classification framework,which fails to model the complex interactions among complementary case materials.To address this issue,we formalize the task as Legal Reading Comprehension according to the legal scenario.Following the working protocol of human judges,LRC predicts the final judgment results based on three types of information,including fact description,plaintiffs pleas,and law articles.Moreover,we propose a novel LRC model,AutoJudge,which captures the complex semantic interactions among facts,pleas,and laws.In experiments,we construct a real-world civil case dataset for LRC.Experimental results on this dataset demonstrate that our model achieves significant improvement over state-of-the-art models.