SECaps: A Sequence Enhanced Capsule Model for Charge Prediction

TL;DR

The paper introduces SECaps, a novel sequence-enhanced capsule network for charge prediction that effectively handles few-shot charges and imbalanced data, outperforming existing methods.

Contribution

It proposes a sequence-enhanced capsule network with an attention residual unit and focal loss to improve charge prediction, especially for few-shot and imbalanced cases.

Findings

SECaps achieves 4.5% and 6.4% improvements in Macro F1 scores.

The model effectively predicts few-shot charges.

Experimental results show superior performance over state-of-the-art methods.

Abstract

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.