Environmental Claim Detection

Dominik Stammbach, Nicolas Webersinke, Julia Anna Bingler, Mathias Kraus, Markus Leippold

Main: Resources and Evaluation Main-poster Paper

Poster Session 3: Resources and Evaluation (Poster)
Conference Room: Frontenac Ballroom and Queen's Quay
Conference Time: July 11, 09:00-10:30 (EDT) (America/Toronto)
Global Time: July 11, Poster Session 3 (13:00-14:30 UTC)
Keywords: nlp datasets
TLDR: To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the...
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Abstract: To transition to a green economy, environmental claims made by companies must be reliable, comparable, and verifiable. To analyze such claims at scale, automated methods are needed to detect them in the first place. However, there exist no datasets or models for this. Thus, this paper introduces the task of environmental claim detection. To accompany the task, we release an expert-annotated dataset and models trained on this dataset. We preview one potential application of such models: We detect environmental claims made in quarterly earning calls and find that the number of environmental claims has steadily increased since the Paris Agreement in 2015.