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        <title>Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation</title>
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    <name type="personal">
        <namePart type="given">Zhang</namePart>
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        <namePart type="family">Ji-Rong</namePart>
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            <title>Proceedings of the 20th Chinese National Conference on Computational Linguistics</title>
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            <namePart type="given">Sheng</namePart>
            <namePart type="family">Li</namePart>
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    <abstract>Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charges has achieved promising results but that on few-shot chargesis still challenging. In this work we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically we extract coarse- and fine-grained features to enhance the model‘s capability on representation based on which the few-shot chargescan be better distinguished. Furthermore we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides our method can work wellon tiny datasets and has better efficiency in the training which provides better applicability in realscenarios.</abstract>
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        <url>https://aclanthology.org/2021.ccl-1.103/</url>
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    <part>
        <date>2021-08</date>
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            <start>1154</start>
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%0 Conference Proceedings
%T Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation
%A Han, Zhang
%A Yutao, Zhu
%A Zhicheng, Dou
%A Ji-Rong, Wen
%Y Li, Sheng
%Y Sun, Maosong
%Y Liu, Yang
%Y Wu, Hua
%Y Liu, Kang
%Y Che, Wanxiang
%Y He, Shizhu
%Y Rao, Gaoqi
%S Proceedings of the 20th Chinese National Conference on Computational Linguistics
%D 2021
%8 August
%I Chinese Information Processing Society of China
%C Huhhot, China
%G eng
%F han-etal-2021-shot
%X Charge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction on high-frequency charges has achieved promising results but that on few-shot chargesis still challenging. In this work we propose a framework with multi-grained features and mutual information for few-shot charge prediction. Specifically we extract coarse- and fine-grained features to enhance the model‘s capability on representation based on which the few-shot chargescan be better distinguished. Furthermore we propose a loss function based on mutual information. This loss function leverages the prior distribution of the charges to tune their weights so the few-shot charges can contribute more on model optimization. Experimental results on several datasets demonstrate the effectiveness and robustness of our method. Besides our method can work wellon tiny datasets and has better efficiency in the training which provides better applicability in realscenarios.
%U https://aclanthology.org/2021.ccl-1.103/
%P 1154-1166

Markdown (Informal)

[Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation](https://aclanthology.org/2021.ccl-1.103/) (Han et al., CCL 2021)

• Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation (Han et al., CCL 2021)

ACL

• Zhang Han, Zhu Yutao, Dou Zhicheng, and Wen Ji-Rong. 2021. Few-Shot Charge Prediction with Multi-Grained Features and MutualInformation. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1154–1166, Huhhot, China. Chinese Information Processing Society of China.