Paper Published at EMNLP

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Our paper “Towards Climate Awareness in NLP Research” has been presented at this years Conference on Empirical Methods in Natural Language Processing (EMNLP). In this work, we discuss the status of climate awareness of NLP and propose means to effectively keep track of climate impact from research.

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.

This is joined work with Daniel Hershcovich (University of Copenhagen), Nicolas Webersinke (FAU Erlangen-Nürnberg), Julia Anna Bingler (Council on Economic Policies), and Markus Leippold (University of Zurich).