用监督对比学习进行图形推理,用于法律判决预测
IEEE transactions on neural networks and learning systems
|January 1, 2024
概括
本研究介绍了GraSCL,这是一种新的图形推理和监督对比学习框架,用于法律判断预测. 它有效地建模了标签依赖性,提高了法律条款,指控和处罚的预测准确性.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 法律判断预测 (LJP) 涉及从案件事实中确定法律条款,指控和处罚.
- 现有的LJP模型经常忽视不同预测任务中的标签依赖性.
- 有效地利用任务和标签之间的关系信息对LJP至关重要.
研究的目的:
- 为LJP提出一个新的框架,GraSCL,它集成了图形推理和监督对比学习 (SCL).
- 通过明确建模标签依赖来解决现有方法的局限性.
- 提高法律判断预测的准确性和效率.
主要方法:
- 在图形推理框架内将LJP转化为节点分类问题.
- 设计了一个图形推理网络,以捕捉依赖结构和关系学习.
- 扩展监督对比学习 (SCL) 到节点级,以提供高效的培训.
- 整合在线硬负面挖掘 (OHNM) 通过专注于具有挑战性的负样本来优化SCL.
主要成果:
- 拟议的GraSCL框架显示了LJP任务的显著改进.
- 图形推理有效地建模了跨任务和跨标签的依赖关系.
- 节点级的SCL和OHNM增强了模型性能,特别是在小批量.
- 在基准数据集上的实验结果验证了与最先进的方法相比的有效性.
结论:
- 通过利用图形推理和对比学习,GraSCL提供了一种强大的法律判断预测方法.
- 建模标签依赖性是提高LJP准确性的关键.
- SCL和OHNM的整合为复杂的预测任务提供了高效和有效的培训策略.
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