基于框架语义的任务导向型自动事实核查
Task-Oriented Automatic Fact-Checking with Frame-Semantics
AI总结：
该研究提出利用框架语义的任务导向型自动事实核查范式，构建PolitiFact试点数据集并开展两项案例研究，证明框架语义可提升证据检索与可解释性，还识别出高影响力框架以指导未来工作。
摘要：
我们提出一种新颖的自动事实核查范式，该范式利用框架语义增强对主张的结构化理解并指导事实核查过程。为此，我们引入一个从PolitiFact提取的真实世界主张试点数据集，该数据集专门针对大规模结构化数据进行了标注。该数据集支持两项案例研究：第一项使用Vote语义框架调查与投票相关的主张，第二项基于经济合作与发展组织（OECD）的数据源探索各种语义框架。我们的研究结果证明，框架语义在改进事实核查的证据检索和可解释性方面具有有效性。最后，我们对事实核查主张中引发的框架进行了调查，识别出高影响力框架以指导该方向的未来工作。
英文摘要：
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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