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.- Anthology ID:
- 2023.acl-short.91
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1051–1066
- Language:
- URL:
- https://aclanthology.org/2023.acl-short.91
- DOI:
- 10.18653/v1/2023.acl-short.91
- Cite (ACL):
- Dominik Stammbach, Nicolas Webersinke, Julia Bingler, Mathias Kraus, and Markus Leippold. 2023. Environmental Claim Detection. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 1051–1066, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Environmental Claim Detection (Stammbach et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/improve-issue-templates/2023.acl-short.91.pdf