@InProceedings{ye-EtAl:2018:N18-1,
  author    = {Ye, Hai  and  Jiang, Xin  and  Luo, Zhunchen  and  Chao, Wenhan},
  title     = {Interpretable Charge Predictions for Criminal Cases: Learning to Generate Court Views from Fact Descriptions},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {1854--1864},
  abstract  = {In this paper, we propose to study the problem of court view generation from the fact description in a criminal case. The task aims to improve the interpretability of charge prediction systems and help automatic legal document generation. We formulate this task as a text-to-text natural language generation (NLG) problem. Sequence-to-sequence model has achieved cutting-edge performances in many NLG tasks. However, due to the non-distinctions of fact descriptions, it is hard for Seq2Seq model to generate charge-discriminative court views. In this work, we explore charge labels to tackle this issue. We propose a label-conditioned Seq2Seq model with attention for this problem, to decode court views conditioned on encoded charge labels. Experimental results show the effectiveness of our method.},
  url       = {http://www.aclweb.org/anthology/N18-1168}
}