Towards Climate Awareness in NLP Research

Hershcovich D, Webersinke N, Kraus M, Bingler JA, Leippold M (2022)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2022

Pages Range: 2480-2494

Conference Proceedings Title: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Event location: Abu Dhabi AE

URI: https://aclanthology.org/2022.emnlp-main.159

Open Access Link: https://aclanthology.org/2022.emnlp-main.159

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.

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How to cite

APA:

Hershcovich, D., Webersinke, N., Kraus, M., Bingler, J.A., & Leippold, M. (2022). Towards Climate Awareness in NLP Research. In Association for Computational Linguistics (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 2480-2494). Abu Dhabi, AE.

MLA:

Hershcovich, Daniel, et al. "Towards Climate Awareness in NLP Research." Proceedings of the EMNLP 2022: The 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi Ed. Association for Computational Linguistics, 2022. 2480-2494.

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