Overcoming language barriers in academia: machine translation tools and a vision for a multilingual future

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1093/biosci/biac062. This is version 3 of this Preprint.

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Authors

Emma Cathleen Steigerwald, Valeria Ramírez-Castañeda, Débora Brandt, Julie Shapiro, András Báldi, Lynne Bowker, Rebecca D. Tarvin

Abstract

Having a central scientific language remains crucial for the advancement and global sharing of science. Nevertheless, maintaining one dominant language also creates barriers to accessing scientific careers and knowledge. From an interdisciplinary perspective, we describe how, when, and why to more readily make scientific literature available in multiple languages through the practice of translation. We broadly review the advantages and limitations of neural machine translation systems and propose that translation can serve as both a short- and long-term solution for making science more resilient, accessible, globally representative, and impactful beyond the academy. We outline immediate actions that individuals and institutions can take to support multilingual science and scientists, including structural changes that encourage and place additional value on translating scientific literature. In the long term, improvements to machine translation technologies and collective efforts to change academic norms can transform a monolingual scientific hub into a more distributed, multilingual scientific network.

DOI

https://doi.org/10.32942/osf.io/m7wfy

Subjects

Communication, Life Sciences, Social and Behavioral Sciences

Keywords

Artificial Intelligence, machine learning, multilingualism, Neural Networks, plain-language abstract, scientific communication

Dates

Published: 2022-03-07 08:58

Last Updated: 2022-05-22 23:45

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License

CC-By Attribution-NonCommercial-NoDerivatives 4.0 International