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