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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | Environmental Claim Detection |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Page Range | 1051 - 1066 |
| Event Title | 61st Annual Meeting of the Association for Computational Linguistics (ACL’23) |
| Event Type | conference |
| Event Location | Toronto, Canada |
| Event Start Date | July 9 - 2023 |
| Event End Date | July 14 - 2023 |
| Series Name | Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics |
| Publisher | Association for Computational Linguistics |
| Abstract Text | 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. |
| Free access at | DOI |
| Digital Object Identifier | 10.18653/v1/2023.acl-short.91 |
| Other Identification Number | merlin-id:24246 |
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