@InProceedings{hu-EtAl:2018:C18-1,
  author    = {Hu, Zikun  and  Li, Xiang  and  Tu, Cunchao  and  Liu, Zhiyuan  and  Sun, Maosong},
  title     = {Few-Shot Charge Prediction with Discriminative Legal Attributes},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, USA},
  publisher = {Association for Computational Linguistics},
  pages     = {487--498},
  abstract  = {Automatic charge prediction aims to predict the final charges according to the fact descriptions in criminal cases and plays a crucial role in legal assistant systems. Existing works on charge prediction perform adequately on those high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. Moreover, these exist many confusing charge pairs, whose fact descriptions are fairly similar to each other. To address these issues, we introduce several discriminative attributes of charges as the internal mapping between fact descriptions and charges. These attributes provide additional information for few-shot charges, as well as effective signals for distinguishing confusing charges. More specifically, we propose an attribute-attentive charge prediction model to infer the attributes and charges simultaneously. Experimental results on real-work datasets demonstrate that our proposed model achieves significant and consistent improvements than other state-of-the-art baselines. Specifically, our model outperforms other baselines by more than $50\%$ in the few-shot scenario. Our codes and datasets can be obtained from \url{https://github.com/thunlp/attribute\_charge}.},
  url       = {http://www.aclweb.org/anthology/C18-1041}
}