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        <title>Learning to Predict Charges for Criminal Cases with Legal Basis</title>
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        <namePart type="given">Bingfeng</namePart>
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        <namePart type="given">Yansong</namePart>
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    <name type="personal">
        <namePart type="given">Jianbo</namePart>
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    <name type="personal">
        <namePart type="given">Xiang</namePart>
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        <namePart type="given">Dongyan</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Learning to Predict Charges for Criminal Cases with Legal Basis
%A Luo, Bingfeng
%A Feng, Yansong
%A Xu, Jianbo
%A Zhang, Xiang
%A Zhao, Dongyan
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F luo-etal-2017-learning
%X 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.
%R 10.18653/v1/D17-1289
%U https://aclanthology.org/D17-1289
%U https://doi.org/10.18653/v1/D17-1289
%P 2727-2736

Markdown (Informal)

[Learning to Predict Charges for Criminal Cases with Legal Basis](https://aclanthology.org/D17-1289) (Luo et al., EMNLP 2017)

• Learning to Predict Charges for Criminal Cases with Legal Basis (Luo et al., EMNLP 2017)

ACL

• Bingfeng Luo, Yansong Feng, Jianbo Xu, Xiang Zhang, and Dongyan Zhao. 2017. Learning to Predict Charges for Criminal Cases with Legal Basis. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2727–2736, Copenhagen, Denmark. Association for Computational Linguistics.