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CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims

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arxiv 2012.00614 v2 pith:KPLRRMHG submitted 2020-12-01 cs.CL cs.AI

Thomas Diggelmann , Jordan Boyd-Graber , Jannis Bulian , Massimiliano Ciaramita , Markus Leippold This is my paper · ORCID
classification cs.CLcs.AI
keywords claimsclimatedatasetclimate-fevercommunityfeverlanguagereal-world
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.

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Cited by 12 Pith papers

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