  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.
