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Towards Climate Awareness in NLP Research

Leippold, Markus; Hershcovich, Daniel; Bingler, Julia Anna; Kraus, Mathias; Webersinke, Nicolas (2022). Towards Climate Awareness in NLP Research. In: Empirical Methods in Natural Language Processing (EMNLP), Abu Dhabi, 7 December 2022 - 11 December 2022. Association for Computational Linguistics, 2480-2494.

Abstract

The climate impact of AI, and NLP research in particular, has become a serious issue given the enormous amount of energy that is increasingly being used for training and running computational models. Consequently, increasing focus is placed on efficient NLP. However, this important initiative lacks simple guidelines that would allow for systematic climate reporting of NLP research. We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact. As a remedy, we propose a climate performance model card with the primary purpose of being practically usable with only limited information about experiments and the underlying computer hardware. We describe why this step is essential to increase awareness about the environmental impact of NLP research and, thereby, paving the way for more thorough discussions.

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 > Computational Theory and Mathematics
Physical Sciences > Computer Science Applications
Physical Sciences > Information Systems
Scope:Discipline-based scholarship (basic research)
Language:English
Event End Date:11 December 2022
Deposited On:03 Aug 2023 12:28
Last Modified:25 Sep 2024 03:37
Publisher:Association for Computational Linguistics
Series Name:Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.18653/v1/2022.emnlp-main.159
Other Identification Number:merlin-id:23707
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  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)

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