Sequence Generation Network Based on Hierarchical Attention for Multi-Charge Prediction

Abstract

The application of multi-label text classification in charge prediction aims at forecasting all kinds of charges related to the content of judgment documents according to the actual situation, which plays a vital role in the judgment of criminal cases. Existing classification algorithms have high accuracy for the single-charge prediction, but their accuracy for the multi-charge prediction is low. To solve this problem, in this paper we introduce a novel hierarchical nested attention structure model with relevant law article information to predict the multi-charge classification of legal judgment documents. By considering the correlation between different charges, the accuracy of multi-charge prediction is greatly improved. Experimental results on real-world datasets demonstrate that our proposed model achieves significant and consistent improvements over other state-of-the-art baselines.