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.
@article{arxiv.2501.13288,
title = {Task-Oriented Automatic Fact-Checking with Frame-Semantics},
author = {Jacob Devasier and Rishabh Mediratta and Akshith Putta and Chengkai Li},
journal= {arXiv preprint arXiv:2501.13288},
year = {2025}
}