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Environmental Claim Detection

Leippold, Markus; Stammbach, Dominik; Webersinke, Nicolas; Bingler, Julia Anna; Kraus, Mathias (2023). Environmental Claim Detection. In: 61st Annual Meeting of the Association for Computational Linguistics (ACL’23), Toronto, Canada, 9 July 2023 - 14 July 2023. Association for Computational Linguistics, 1051-1066.

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.

Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Physical Sciences > Computer Science Applications
Social Sciences & Humanities > Linguistics and Language
Social Sciences & Humanities > Language and Linguistics
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:14 July 2023
Deposited On:10 Jan 2024 11:11
Last Modified:28 Sep 2024 03:30
Publisher:Association for Computational Linguistics
Series Name:Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.18653/v1/2023.acl-short.91
Other Identification Number:merlin-id:24246
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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