Verifying political claims is a challenging task, as politicians can use various tactics to subtly misrepresent the facts for their agenda. Existing automatic fact-checking systems fall short here, and their predictions like "half-true" are not very useful in isolation, since it is unclear which parts of a claim are true or false. In this work, we focus on decomposing a complex claim into a comprehensive set of yes-no subquestions whose answers influence the veracity of the claim. We present CLAIMDECOMP, a dataset of decompositions for over 1000 claims. Given a claim and its verification paragraph written by fact-checkers, our trained annotators write subquestions covering both explicit propositions of the original claim and its implicit facets, such as additional political context that changes our view of the claim's veracity. We study whether state-of-the-art pre-trained models can learn to generate such subquestions. Our experiments show that these models generate reasonable questions, but predicting implied subquestions based only on the claim (without consulting other evidence) remains challenging. Nevertheless, we show that predicted subquestions can help identify relevant evidence to fact-check the full claim and derive the veracity through their answers, suggesting that claim decomposition can be a useful piece of a fact-checking pipeline.
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This content will become publicly available on December 17, 2023
Comparative Reasoning for Knowledge Graph Fact Checking
Knowledge graph has been widely used in fact checking, owing to its capability to provide crucial background knowledge to help verify claims. Traditional fact checking works mainly focus on analyzing a single claim but have largely ignored analysis on the semantic consistency of pair-wise claims, despite its key importance in the real-world applications, e.g., multimodal fake news detection. This paper proposes a graph neural network based model INSPECTOR for pair-wise fact checking. Given a pair of claims, INSPECTOR aims to detect the potential semantic inconsistency of the input claims. The main idea of INSPECTOR is to use a graph attention neural network to learn a graph embedding for each claim in the pair, then use a tensor neural network to classify this pair of claims as consistent vs. inconsistent. The experiment results show that our algorithm outperforms state-of-the-art methods, with a higher accuracy and a lower variance.
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- NSF-PAR ID:
- 10428926
- Date Published:
- Journal Name:
- 2022 IEEE International Conference on Big Data (Big Data)
- Page Range / eLocation ID:
- 2309 to 2312
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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