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.
Original language | English |
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Title of host publication | Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing |
Publisher | Association for Computational Linguistics |
Publication date | 2022 |
Pages | 2480-2494 |
Publication status | Published - 2022 |
Event | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 - Abu Dhabi, United Arab Emirates Duration: 7 Dec 2022 → 11 Dec 2022 |
Conference
Conference | 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 |
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Country/Territory | United Arab Emirates |
City | Abu Dhabi |
Period | 07/12/2022 → 11/12/2022 |
Bibliographical note
Publisher Copyright:© 2022 Association for Computational Linguistics.