SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge pre-diction plays an important role in assisting judges and lawyers to improve the effi-ciency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with lim-ited cases. On the other hand, some works have shown the benefits of capsule net-work, which is a powerful technique. This motivates us to propose a Sequence En-hanced Capsule model, dubbed as SECaps model, to relieve this problem. More specifically, we propose a new basic structure, seq-caps layer, to enhance capsule by taking sequence information in to account. In addition, we construct our SE-Caps model by making use of seq-caps layer. Comparing the state-of-the-art meth-ods, our SECaps model achieves 4.5% and 6.4% F1 promotion in two real-world datasets, Criminal-S and Criminal-L, respectively. The experimental results consis-tently demonstrate the superiorities and competitiveness of our proposed model.