Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge prediction plays a critical role in assisting judges and lawyers to improve the efficiency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence information and spatial information of legal texts simultaneously. Then we design a attention residual unit, which provides auxiliary information for charge prediction. In addition, our SECaps model introduces focal loss, which relieves the problem of imbalanced charges. Comparing the state-of-the-art methods, our SECaps model obtains 4.5% and 6.4% absolutely considerable improvements under Macro F1 in Criminal-S and Criminal-L respectively. The experimental results consistently demonstrate the superiorities and competitiveness of our proposed model.
翻译:自动电荷预测旨在根据特定刑事案件的事实描述预测适当的最终指控; 自动电荷预测在协助法官和律师提高法律裁决效率方面发挥着关键作用,因此引起了很大的注意; 然而,大多数关于自动电荷预测的现有工程都对高频电荷进行了充分的预测,但尚不能对有限案件作出几发电荷预测; 在本文件中,我们提出了一个称为SECaps模型的序列增强电荷模型,以缓解这一问题; 具体地说,在胶囊网络工作之后,我们提出了后端层,该层同时考虑法律文本的顺序信息和空间信息; 然后我们设计了一个关注剩余单元,为收费预测提供辅助信息; 此外,我们的SECaps模型引入了焦点损失,从而缓解了不平衡电费问题; 与最新的方法相比,我们的SECaps模型在犯罪-S和刑事-L的Mroc F1下分别获得了4.5%和6.4%的绝对显著改进。 实验结果始终表明我们提议的模型的优越性和竞争力。