  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. We will publish all
source codes and datasets of this work on \urlgithub.com for further research.
