A legal judgment prediction model based on knowledge fusion and dependency masking
Yishan Chen, Xiaoyi Zhu, Zhiyun Zeng, Pengfei Wang, Xinhua Zhu
TL;DR
This paper introduces a new legal judgment prediction model that improves accuracy by combining knowledge fusion and dependency masking techniques.
Contribution
The novel approach integrates CNN-based refinement, differential attention, and dependency masking for better legal judgment prediction.
Findings
The model outperforms existing methods in extracting core judicial knowledge from legal documents.
The proposed dependency masking mechanism effectively filters erroneous information in multi-task frameworks.
Experiments on real-world datasets show the superiority of the new model.
Abstract
Legal Judgment Prediction (LJP) is a core task in Legal AI systems, which aims to predict law articles, charges, and term-of-penalty from case facts. While existing deep-learning-based LJP approaches for civil law systems have achieved certain progress, they still suffer from two key limitations: (1) insufficient deep understanding and effective utilization of external judicial knowledge; and (2) the lack of effective strategies to filter out erroneous dependency information in multi-task LJP frameworks. To address these challenges, we propose a legal judgment prediction model based on knowledge fusion and dependency masking. Specifically, we first integrate a CNN-based local semantic refinement component into the existing BERT-based legal knowledge extraction method, thereby enabling the model to further extract the core knowledge embedded in judicial documents. Then, we introduce…
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Taxonomy
TopicsArtificial Intelligence in Law · Topic Modeling · Explainable Artificial Intelligence (XAI)