Summary of Distinguish Confusion in Legal Judgment Prediction Via Revised Relation Knowledge, by Nuo Xu et al.
Distinguish Confusion in Legal Judgment Prediction via Revised Relation Knowledge
by Nuo Xu, Pinghui Wang, Junzhou Zhao, Feiyang Sun, Lin Lan, Jing Tao, Li Pan, Xiaohong Guan
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Legal Judgment Prediction (LJP) aims to automatically predict law case judgments from text descriptions. The “confusing law articles” problem occurs when similar cases are misjudged due to data imbalance. Existing solutions ignore posterior semantic similarity between law articles with high similarity, which this work addresses. The proposed end-to-end model, D-LADAN, constructs a graph among law articles based on their text definitions and uses graph distillation operation (GDO) to distinguish highly similar ones. Additionally, it employs momentum-updated memory mechanism to dynamically sense posterior similarity between law articles and weighted GDO to adaptively capture distinctions. Experimental results demonstrate that D-LADAN outperforms state-of-the-art methods in accuracy and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to predict the outcome of a court case based on its description. Sometimes, similar cases are misjudged because of a problem with the data. The researchers propose a new way to solve this issue by creating a graph that connects similar law articles and uses it to distinguish between them. They also use a memory mechanism to adjust for any biases in the data. The results show that their method is more accurate and robust than existing methods. |
Keywords
» Artificial intelligence » Distillation
