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Filtered Union Bibliography

This automatically-generated file contains references from the main union bibliography that have been filtered for a single tag. Do not edit this file; instead, please update the main bibliography and tag references appropriately to have them show up here. Thank you!

The papers are listed in the same order as the main bibliography; e.g., by year of publication / release; then by surname / name of the first author.

Environmental Impact

  • Przybyła P., and Shardlow M. (2022). Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3853–3863, Dublin, Ireland. Association for Computational Linguistics. [paper] Environmental Impact published

  • Bannour, N., Ghannay, S., Névéol, A. and Ligozat, A.-L. 2021. Evaluating the carbon footprint of NLP methods: a survey and analysis of existing tools. In Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing, pages 11–21, Virtual. Association for Computational Linguistics. [paper] Environmental Impact published

  • Lannelongue, L., Grealey, J., & Inouye, M. (2021). Green algorithms: Quantifying the carbon footprint of computation. Advanced Science, 2100707. doi:10.1002/advs.202100707. [paper] Environmental Impact published

  • Anthony, L. F. W., Kanding, B., & Selvan, R. (2020). Carbontracker: Tracking and predicting the carbon footprint of training deep learning models. arXiv preprint arXiv:2007.03051. [paper] Environmental Impact preprint

  • Henderson, P., Hu, J., Romoff, J., Brunskill, E., Jurafsky, D., & Pineau, J. (2020). Towards the systematic reporting of the energy and carbon footprints of machine learning. Journal of Machine Learning Research, 21(248), 1-43. [paper] Environmental Impact published

  • Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12), 54-63. [paper] Environmental Impact published

  • Trebaol, M. J. T., Hartley, M.-A., & Ghadikolaei, H. S. (2020). A tool to quantify and report the carbon footprint of machine learning computations and communication in academia and healthcare. Infoscience EPFL: record 278189. [report] Environmental Impact report

  • Lacoste A., Luccioni A., Schmidt V., & Dandres T. (2019). Quantifying the carbon emissions of machine learning. In Climate Change workshop, NeurIPS 2019. [paper] Environmental Impact published

  • Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3645–3650, Florence, Italy. Association for Computational Linguistics. doi:10.18653/v1/P19-1355. [paper] Environmental Impact published