@InProceedings{luo-EtAl:2017:EMNLP2017,
  author    = {Luo, Bingfeng  and  Feng, Yansong  and  Xu, Jianbo  and  Zhang, Xiang  and  Zhao, Dongyan},
  title     = {Learning to Predict Charges for Criminal Cases with Legal Basis},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2727--2736},
  abstract  = {The charge prediction task is to determine appropriate charges for a given
	case, which is helpful for legal assistant systems where the user input is fact
	description. We argue that relevant law articles play an important role in this
	task, and therefore propose an attention-based neural network method to jointly
	model the charge prediction task and the relevant article extraction task in a
	unified framework. The experimental results show that, besides providing legal
	basis, the relevant articles can also clearly improve the charge prediction
	results, and our full model can effectively predict appropriate charges for
	cases with different expression styles.},
  url       = {https://www.aclweb.org/anthology/D17-1289}
}