TY - GEN

T1 - Should We Ban English NLP for a Year?

AU - Søgaard, Anders

N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.

PY - 2022

Y1 - 2022

N2 - Around two thirds of NLP research at top venues is devoted exclusively to developing technology for speakers of English, most speech data comes from young urban speakers, and most texts used to train language models come from male writers. These biases feed into consumer technologies to widen existing inequality gaps, not only within, but also across, societies. Many have argued that it is almost impossible to mitigate inequality amplification. I argue that, on the contrary, it is quite simple to do so, and that counter-measures would have little-to-no negative impact, except for, perhaps, in the very short term.

AB - Around two thirds of NLP research at top venues is devoted exclusively to developing technology for speakers of English, most speech data comes from young urban speakers, and most texts used to train language models come from male writers. These biases feed into consumer technologies to widen existing inequality gaps, not only within, but also across, societies. Many have argued that it is almost impossible to mitigate inequality amplification. I argue that, on the contrary, it is quite simple to do so, and that counter-measures would have little-to-no negative impact, except for, perhaps, in the very short term.

UR - http://www.scopus.com/inward/record.url?scp=85149433455&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:85149433455

SP - 5254

EP - 5260

BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

PB - Association for Computational Linguistics

T2 - 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

Y2 - 7 December 2022 through 11 December 2022

ER -