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

• URL: http://arxiv.org/abs/2012.00614v2
• Date: Sat, 2 Jan 2021 16:07:48 GMT
• Title: CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims
• Authors: Thomas Diggelmann and Jordan Boyd-Graber and Jannis Bulian and Massimiliano Ciaramita and Markus Leippold
• Abstract summary: We introduce CLIMATE-FEVER, a new dataset for verification of climate change-related claims. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the textscfever framework.
• Score: 4.574830585715129
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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