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Legal judgment prediction via optimized multi-task learning fusing similarity correlation.

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Title: Legal judgment prediction via optimized multi-task learning fusing similarity correlation.
Authors: Guo, Xiaoding1 (AUTHOR), Zao, Feifei1 (AUTHOR), Shen, Zhuo2 (AUTHOR), Zhang, Lei1 (AUTHOR) zhanglei@henu.edu.cn
Source: Applied Intelligence. Nov2023, Vol. 53 Issue 21, p26205-26229. 25p.
Subjects: Legal judgments, Cable News Network, Artificial intelligence, Computational complexity, Forecasting
Abstract: The use of computer-assisted legal judgment prediction (LJP) is a current research hotspot, driven by advances in artificial intelligence technology. Previous LJP methods have mainly relied on feature models and emphasized parameter sharing within the coding layer, ignoring progressive sequential relationships between LJP subtasks as well as potential similarity correlations between cases. These limitations have hindered the improvement of the accuracy of LJP methods. This article proposes an LJP algorithm, called MTL-LJP, based on optimised multi-task learning that fuses similarity correlations. MTL-LJP uses EnMo to encode cases and SiMa to compute similarity matrices between cases. MuTa, which is based on multi-task learning, is used to predict LJP subtasks. EnMo vectorises case facts from multiple perspectives using encoders based on CNN, Bi-GRU, Bi-GRU with attention mechanism and MMoE. SiMa computes centroids and distance vectors based on historical case labels and LJP subtask predictions, allowing the computation of similarity matrices for each subtask with low computational complexity. MuTa predicts subsequent subtasks by using the intermediate results of previous subtasks through the forward auxiliary mechanisms. MuTa modifies predictions by correlating the similarity of cases. Experimental results on several real case datasets show that MTL-LJP outperforms previous methods on LJP subtasks. [ABSTRACT FROM AUTHOR]
Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Legal judgment prediction via optimized multi-task learning fusing similarity correlation.
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  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Xiaoding%22">Guo, Xiaoding</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zao%2C+Feifei%22">Zao, Feifei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Zhuo%22">Shen, Zhuo</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Lei%22">Zhang, Lei</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhanglei@henu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Applied+Intelligence%22">Applied Intelligence</searchLink>. Nov2023, Vol. 53 Issue 21, p26205-26229. 25p.
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  Data: <searchLink fieldCode="DE" term="%22Legal+judgments%22">Legal judgments</searchLink><br /><searchLink fieldCode="DE" term="%22Cable+News+Network%22">Cable News Network</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computational+complexity%22">Computational complexity</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: The use of computer-assisted legal judgment prediction (LJP) is a current research hotspot, driven by advances in artificial intelligence technology. Previous LJP methods have mainly relied on feature models and emphasized parameter sharing within the coding layer, ignoring progressive sequential relationships between LJP subtasks as well as potential similarity correlations between cases. These limitations have hindered the improvement of the accuracy of LJP methods. This article proposes an LJP algorithm, called MTL-LJP, based on optimised multi-task learning that fuses similarity correlations. MTL-LJP uses EnMo to encode cases and SiMa to compute similarity matrices between cases. MuTa, which is based on multi-task learning, is used to predict LJP subtasks. EnMo vectorises case facts from multiple perspectives using encoders based on CNN, Bi-GRU, Bi-GRU with attention mechanism and MMoE. SiMa computes centroids and distance vectors based on historical case labels and LJP subtask predictions, allowing the computation of similarity matrices for each subtask with low computational complexity. MuTa predicts subsequent subtasks by using the intermediate results of previous subtasks through the forward auxiliary mechanisms. MuTa modifies predictions by correlating the similarity of cases. Experimental results on several real case datasets show that MTL-LJP outperforms previous methods on LJP subtasks. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Intelligence is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10489-023-04904-x
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        Text: English
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      – SubjectFull: Legal judgments
        Type: general
      – SubjectFull: Cable News Network
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Computational complexity
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      – SubjectFull: Forecasting
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      – TitleFull: Legal judgment prediction via optimized multi-task learning fusing similarity correlation.
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              Text: Nov2023
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              Y: 2023
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