Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences

Piotr Przybyła, Matthew Shardlow


Abstract
The environmental costs of research are progressively important to the NLP community and their associated challenges are increasingly debated. In this work, we analyse the carbon cost (measured as CO2-equivalent) associated with journeys made by researchers attending in-person NLP conferences. We obtain the necessary data by text-mining all publications from the ACL anthology available at the time of the study (n=60,572) and extracting information about an author’s affiliation, including their address. This allows us to estimate the corresponding carbon cost and compare it to previously known values for training large models. Further, we look at the benefits of in-person conferences by demonstrating that they can increase participation diversity by encouraging attendance from the region surrounding the host country. We show how the trade-off between carbon cost and diversity of an event depends on its location and type. Our aim is to foster further discussion on the best way to address the joint issue of emissions and diversity in the future.
Anthology ID:
2022.findings-acl.304
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3853–3863
Language:
URL:
https://aclanthology.org/2022.findings-acl.304
DOI:
10.18653/v1/2022.findings-acl.304
Bibkey:
Cite (ACL):
Piotr Przybyła and Matthew Shardlow. 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.
Cite (Informal):
Using NLP to quantify the environmental cost and diversity benefits of in-person NLP conferences (Przybyła & Shardlow, Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.304.pdf
Video:
 https://aclanthology.org/2022.findings-acl.304.mp4
Code
 piotrmp/nlp_geography