He, Congqing (2025) Knowledge-Enhanced Deep Neural Network For Legal Judgment Prediction And Explanation. PhD thesis, Universiti Sains Malaysia.
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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.
| Item Type: | Thesis (PhD) |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA75.5-76.95 Electronic computers. Computer science |
| Divisions: | Pusat Pengajian Sains Komputer (School of Computer Sciences) > Thesis |
| Depositing User: | Mr Aizat Asmawi Abdul Rahim |
| Date Deposited: | 29 Jun 2026 07:40 |
| Last Modified: | 29 Jun 2026 07:50 |
| URI: | http://eprints.usm.my/id/eprint/64503 |
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