IEEE Access (Jan 2020)

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

  • Kongfan Zhu,
  • Baosen Ma,
  • Tianhuan Huang,
  • Zeqiang Li,
  • Haoyang Ma,
  • Yujun Li

DOI
https://doi.org/10.1109/ACCESS.2020.2998486
Journal volume & issue
Vol. 8
pp. 109315 – 109324

Abstract

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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.

Keywords