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    <titleInfo>
        <title>Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge</title>
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    <name type="personal">
        <namePart type="given">Xu</namePart>
        <namePart type="family">Zhuopeng</namePart>
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    <name type="personal">
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        <namePart type="family">Yinlin</namePart>
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    <name type="personal">
        <namePart type="given">Wang</namePart>
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    <name type="personal">
        <namePart type="given">Fanxu</namePart>
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    <name type="personal">
        <namePart type="given">Lai</namePart>
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            <title>Proceedings of the 19th Chinese National Conference on Computational Linguistics</title>
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            <publisher>Chinese Information Processing Society of China</publisher>
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    <abstract>Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.</abstract>
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        <date>2020-oct</date>
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            <start>1132</start>
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%0 Conference Proceedings
%T Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge
%A Zhuopeng, Xu
%A Xia, Li
%A Yinlin, Li
%A Zihan, Wang
%A Yujie, Fanxu
%A Xiaoyan, Lai
%S Proceedings of the 19th Chinese National Conference on Computational Linguistics
%D 2020
%8 oct
%I Chinese Information Processing Society of China
%C Haikou, China
%G English
%F zhuopeng-etal-2020-multi
%X Legal Judgement Prediction has attracted more and more attention in recent years. One of the challenges is how to design a model with better interpretable prediction results. Previous studies have proposed different interpretable models based on the generation of court views and the extraction of charge keywords. Different from previous work, we propose a multi-task legal judgement prediction model which combines a subtask of the seriousness of charges. By introducing this subtask, our model can capture the attention weights of different terms of penalty corresponding to the charges and give more attention to the correct terms of penalty in the fact descriptions. Meanwhile, our model also incorporates the position of defendant making it capable of giving attention to the contextual information of the defendant. We carry several experiments on the public CAIL2018 dataset. Experimental results show that our model achieves better or comparable performance on three subtasks compared with the baseline models. Moreover, we also analyze the interpretable contribution of our model.
%U https://aclanthology.org/2020.ccl-1.105
%P 1132-1142

Markdown (Informal)

[Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge](https://aclanthology.org/2020.ccl-1.105) (Zhuopeng et al., CCL 2020)

• Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge (Zhuopeng et al., CCL 2020)

ACL

• Xu Zhuopeng, Li Xia, Li Yinlin, Wang Zihan, Fanxu Yujie, and Lai Xiaoyan. 2020. Multi-task Legal Judgement Prediction Combining a Subtask of Seriousness of Charge. In Proceedings of the 19th Chinese National Conference on Computational Linguistics, pages 1132–1142, Haikou, China. Chinese Information Processing Society of China.