用语义框架提升事实核查的结构化理解与可解释性
Task-Oriented Automatic Fact-Checking with Frame-Semantics
- 基于语义框架解析声明，构建结构化理解
- 在真实数据集上验证了证据检索准确率提升
- 识别高影响力语义框架，指导未来研究方向
我们提出一种新型自动事实核查范式，利用语义框架增强对声明的结构化理解，并指导核查过程。为此，我们构建了一个来自PolitiFact的真实声明数据集，专门标注用于大规模结构化数据。该数据集支撑两个案例研究：第一个聚焦投票相关声明，使用投票语义框架；第二个基于OECD数据，探索多种语义框架。结果表明，语义框架能有效提升证据检索效果与核查可解释性。最后，我们调查了已核查声明中激发的语义框架，识别出高影响力框架，为后续研究提供方向。
原文摘要 · Abstract (English)
We propose a novel paradigm for automatic fact-checking that leverages frame semantics to enhance the structured understanding of claims and guide the process of fact-checking them. To support this, we introduce a pilot dataset of real-world claims extracted from PolitiFact, specifically annotated for large-scale structured data. This dataset underpins two case studies: the first investigates voting-related claims using the Vote semantic frame, while the second explores various semantic frames based on data sources from the Organisation for Economic Co-operation and Development (OECD). Our findings demonstrate the effectiveness of frame semantics in improving evidence retrieval and explainability for fact-checking. Finally, we conducted a survey of frames evoked in fact-checked claims, identifying high-impact frames to guide future work in this direction.
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