Towards Climate Awareness in NLP Research
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Towards Climate Awareness in NLP Research. / Hershcovich, Daniel; Webersinke, Nicolas; Kraus, Mathias; Bingler, Julia Anna; Leippold, Markus.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2022. p. 2480-2494.Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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TY - GEN
T1 - Towards Climate Awareness in NLP Research
AU - Hershcovich, Daniel
AU - Webersinke, Nicolas
AU - Kraus, Mathias
AU - Bingler, Julia Anna
AU - Leippold, Markus
N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85149436101&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:85149436101
SP - 2480
EP - 2494
BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
PB - Association for Computational Linguistics
T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022
Y2 - 7 December 2022 through 11 December 2022
ER -
ID: 339849096