Distinguish Confusing Law Articles for Legal Judgment Prediction
- URL: http://arxiv.org/abs/2004.02557v3
- Date: Thu, 23 Apr 2020 13:20:23 GMT
- Title: Distinguish Confusing Law Articles for Legal Judgment Prediction
- Authors: Nuo Xu, Pinghui Wang, Long Chen, Li Pan, Xiaoyan Wang, Junzhou Zhao
- Abstract summary: Legal Judgment Prediction (LJP) is the task of automatically predicting a law case's judgment results given a text describing its facts.
We present an end-to-end model, LADAN, to solve the task of LJP.
- Score: 30.083642130015317
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Legal Judgment Prediction (LJP) is the task of automatically predicting a law
case's judgment results given a text describing its facts, which has excellent
prospects in judicial assistance systems and convenient services for the
public. In practice, confusing charges are frequent, because law cases
applicable to similar law articles are easily misjudged. For addressing this
issue, the existing method relies heavily on domain experts, which hinders its
application in different law systems. In this paper, we present an end-to-end
model, LADAN, to solve the task of LJP. To distinguish confusing charges, we
propose a novel graph neural network to automatically learn subtle differences
between confusing law articles and design a novel attention mechanism that
fully exploits the learned differences to extract compelling discriminative
features from fact descriptions attentively. Experiments conducted on
real-world datasets demonstrate the superiority of our LADAN.
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