论文标题
区分令人困惑的法律文章进行法律判决预测
Distinguish Confusing Law Articles for Legal Judgment Prediction
论文作者
论文摘要
法律判决预测（LJP）是自动预测法律案件的判决结果的任务，因为文本描述了其事实，该事实在司法援助系统方面具有良好的前景，并为公众提供便利的服务。在实践中，频繁的指控很混乱，因为适用于类似法律文章的法律案件很容易被判断。为了解决此问题，现有方法在很大程度上取决于领域专家，这阻碍了其在不同的法律系统中的应用。在本文中，我们提出了一个端到端模型Ladan，以解决LJP的任务。为了区分令人困惑的电荷，我们提出了一个新颖的图神经网络，以自动学习混乱的法律文章和设计一种新颖的注意机制之间的微妙差异，该机制充分利用了学习的差异，以认真地从事实描述中提取引人注目的歧视性特征。在现实世界数据集上进行的实验证明了我们的Ladan的优势。
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.
