启发式异质图推理网络用于事实验证
概括
启发式异质图推理网络 (H2GRN) 通过将语言和逻辑证据联系起来来改善基于表的事实验证. 这种方法增强了一致性特征,以便更准确地验证索赔.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 计算机科学 计算机科学
背景情况:
- 现有的基于表格的事实验证方法分别分析语言和逻辑证据.
- 这些证据类型之间的有限相互作用阻碍了提取有价值的一致性特征.
研究的目的:
- 为增强基于表的事实验证提出启发式异质图推理网络 (H2GRN).
- 加强语言和逻辑证据之间的关联,以提高特征提取的一致性.
主要方法:
- 使用索赔语义来引导程序表子图连接和程序逻辑来扩展索赔表子图连接,构建了一个启发式异质图.
- 设计多视图推理网络,包括局部视图多跳转知识推理 (MKR) 和全球视图双重注意网络 (DAN).
- 采用一致性融合层来协调不同的证据并捕获共享的一致证据.
主要成果:
- 通过加强语言和逻辑证据之间的关联,H2GRN有效地捕获共享的一致证据.
- 拟议的方法增强了索赔表和程序表子图之间的连接和相互作用.
- 在TABFACT和FEVEROUS数据集上的实验证明了H2GRN.的有效性.
结论:
- H2GRN通过图形推理整合语言和逻辑证据,为基于表的事实验证提供了一种新的方法.
- 该方法提高了识别一致证据的能力,这对于准确的索赔验证至关重要.
- 这些发现突显了复杂数据验证任务的启发式图形构建和多视图推理的潜力.
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