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Knowledge-Enhanced Deep Neural Network For Legal Judgment Prediction And Explanation

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dc.contributor.authorHe, Congqing
dc.date.accessioned2026-06-29T07:33:39Z
dc.date.available2026-06-29T07:33:39Z
dc.date.issued2025-09
dc.description.abstractThis study aims to bridge the gap by developing the JuriSim framework to enhance the performance and explainability of LJP. Firstly, we propose a rationale generation in the JuriSim framework by introducing event chains as auxiliary knowledge. This enhances the model’s ability to focus on important legal events when generating rationales, thereby improving the effectiveness of legal judgment explanations. Secondly, we propose a dual residual cross-attention mechanism that integrates knowledge of rationales and legal events with the fact description.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/24520
dc.language.isoen
dc.subjectKnowledge-Enhanced Deep Neural Network
dc.subjectLegal Judgment Prediction And Explanation
dc.titleKnowledge-Enhanced Deep Neural Network For Legal Judgment Prediction And Explanation
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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