An Approach Based on Cross-Attention Mechanism and Label-Enhancement Algorithm for Legal Judgment Prediction

Junyi Chen, Xuanqing Zhang, Xiabing Zhou (), Yingjie Han and Qinglei Zhou
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Junyi Chen: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Xuanqing Zhang: School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo 454003, China
Xiabing Zhou: School of Computer Science and Technology, Soochow University, Suzhou 215006, China
Yingjie Han: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
Qinglei Zhou: School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Mathematics, 2023, vol. 11, issue 9, 1-19

Abstract: Legal Judgment Prediction aims to automatically predict judgment outcomes based on descriptions of legal cases and established law articles, and has received increasing attention. In the preliminary work, several problems still have not been adequately solved. One is how to utilize limited but valuable label information. Existing methods mostly ignore the gap between the description of established articles and cases, but directly integrate them. Second, most studies ignore the mutual constraint among the subtasks, such as logically or semantically, each charge is only related to some specific articles. To address these issues, we first construct a crime similarity graph and then perform a distillation operation to collect discriminate keywords for each charge. Furthermore, we fuse these discriminative keywords instead of established article descriptions into case embedding with a cross-attention mechanism to obtain deep semantic representations of cases incorporating label information. Finally, under a constraint among subtasks, we optimize the one-hot representation of ground-truth labels to guarantee consistent results across the subtasks based on the label-enhancement algorithm. To verify the effectiveness and robustness of our framework, we conduct extensive experiments on two public datasets. The experimental results show that the proposed method outperforms the state-of-art models by 3.89%/7.92% and 1.23%/2.50% in the average MF1-score of the subtasks on CAIL-Small/Big, respectively.

Keywords: legal judgment prediction; cross-attention; label-enhancement algorithm; multi-task learning; graph convolutional network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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