ACL 2023short30 citations
Environmental Claim Detection
Dominik Stammbach, Nicolas Webersinke, Julia Bingler, Mathias Kraus, Markus Leippold
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.
BibTeX
@inproceedings{stammbach-etal-2023-environmental,
title = "Environmental Claim Detection",
author = "Stammbach, Dominik and
Webersinke, Nicolas and
Bingler, Julia and
Kraus, Mathias and
Leippold, Markus",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-short.91/",
doi = "10.18653/v1/2023.acl-short.91",
pages = "1051--1066"
}