Publication: Knowledge-Enhanced Deep Neural Network For Legal Judgment Prediction And Explanation
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Date
2025-09
Authors
He, Congqing
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Abstract
This 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.
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Keywords
Knowledge-Enhanced Deep Neural Network , Legal Judgment Prediction And Explanation