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        <title>Few-Shot Charge Prediction with Discriminative Legal Attributes</title>
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        <namePart type="given">Maosong</namePart>
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    <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 https://github.com/thunlp/attribute_charge.</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Few-Shot Charge Prediction with Discriminative Legal Attributes
%A Hu, Zikun
%A Li, Xiang
%A Tu, Cunchao
%A Liu, Zhiyuan
%A Sun, Maosong
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F hu-etal-2018-shot
%X 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 https://github.com/thunlp/attribute_charge.
%U https://aclanthology.org/C18-1041
%P 487-498

Markdown (Informal)

[Few-Shot Charge Prediction with Discriminative Legal Attributes](https://aclanthology.org/C18-1041) (Hu et al., COLING 2018)

• Few-Shot Charge Prediction with Discriminative Legal Attributes (Hu et al., COLING 2018)

ACL

• Zikun Hu, Xiang Li, Cunchao Tu, Zhiyuan Liu, and Maosong Sun. 2018. Few-Shot Charge Prediction with Discriminative Legal Attributes. In Proceedings of the 27th International Conference on Computational Linguistics, pages 487–498, Santa Fe, New Mexico, USA. Association for Computational Linguistics.