MVE-FLK: A multi-task legal judgment prediction via multi-view encoder fusing legal keywords

Yang, Shuxin; Tong, Suxin; Zhu, Guangli; Cao, Jie; Wang, Youquan; Xue, Zhengfa; Sun, Hongliang; Yu, Wen

doi:10.1016/j.knosys.2021.107960

Cited by 11 publications

(1 citation statement)

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“…For example, [6] investigated several discriminative legal attributes to improve the accuracy of low-frequency charges. Other studies focus on jointly modeling multiple subtasks by some novel multi-task legal judgment prediction frameworks in LJP [15], [16]. [17] proposed a TopJudge model, which made use of the relationship between subtasks in LJP.…”

Section: Introductionmentioning

confidence: 99%

Automatic Discovery of Controversial Legal Judgments by an Entropy-Based Measurement (S)

Zhou,

Leng,

Wang

et al. 2023

International Conferences on Software Engineering and Knowledge Engineering

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The judgment of controversial cases has always been an important judicial issue, but it is not easy to discover them in practice. In this paper, based on 1,361,354 legal instruments data collected from China Judgments Online, we adopt a deep learning framework to classify 147 different kinds of crimes. The proposed method has three critical steps: 1) We adopt a deep learning model to predict crime categorization; 2) With the trained model, each case is given a score vector which represents the probability that it belongs to each crime; 3) With the probability score, we develop an entropy-based index to measure the controversy of each case. We find that the larger the entropy, the more inconsistent the result given by the model based on the first instance judgment. To verify the proposed entropy measure, we provide 1) two-sided evidence based on second instance judgments; 2) comparison with some baseline models. Both confirm the practical usefulness of the entropy measure. Our results indicate that the proposed framework has an ability to discover potentially controversial cases. It should be noted that the goal of this study is not to substitute the model result for the judge's decision, but to provide a guiding reference for the judicial practice of sentencing.

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