Towards Climate Awareness in NLP Research

Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Bingler, Markus Leippold

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, and present a quantitative survey to demonstrate this. 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.
Anthology ID:
2022.emnlp-main.159
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2480–2494
Language:
URL:
https://aclanthology.org/2022.emnlp-main.159
DOI:
10.18653/v1/2022.emnlp-main.159
Bibkey:
Cite (ACL):
Daniel Hershcovich, Nicolas Webersinke, Mathias Kraus, Julia Bingler, and Markus Leippold. 2022. Towards Climate Awareness in NLP Research. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 2480–2494, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Towards Climate Awareness in NLP Research (Hershcovich et al., EMNLP 2022)
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PDF:
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