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| Type | Conference or Workshop Paper |
| Scope | Discipline-based scholarship |
| Published in Proceedings | Yes |
| Title | A dataset for detecting real-world environmental claims |
| 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 | 1 - 14 |
| 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 |
| Place of Publication | Toronto, Canada |
| Publisher | arxiv.org |
| Abstract Text | In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies. We train and release baseline models for detecting environmental claims using this new dataset. We further preview potential applications of our dataset: We use our fine-tuned model to detect environmental claims made in answer sections of quarterly earning calls between 2012 and 2020 - and we find that the amount of environmental claims steadily increased since the Paris Agreement in 2015. |
| Free access at | DOI |
| Official URL | https://arxiv.org/abs/2209.00507v1 |
| Digital Object Identifier | 10.48550/arXiv.2209.00507 |
| Other Identification Number | merlin-id:23708 |
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