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Languages with more speakers tend to be harder to (machine-)learn

Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study differen...

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Autores principales: Koplenig, Alexander, Wolfer, Sascha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613286/
https://www.ncbi.nlm.nih.gov/pubmed/37898699
http://dx.doi.org/10.1038/s41598-023-45373-z
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author Koplenig, Alexander
Wolfer, Sascha
author_facet Koplenig, Alexander
Wolfer, Sascha
author_sort Koplenig, Alexander
collection PubMed
description Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn.
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spelling pubmed-106132862023-10-30 Languages with more speakers tend to be harder to (machine-)learn Koplenig, Alexander Wolfer, Sascha Sci Rep Article Computational language models (LMs), most notably exemplified by the widespread success of OpenAI's ChatGPT chatbot, show impressive performance on a wide range of linguistic tasks, thus providing cognitive science and linguistics with a computational working model to empirically study different aspects of human language. Here, we use LMs to test the hypothesis that languages with more speakers tend to be easier to learn. In two experiments, we train several LMs—ranging from very simple n-gram models to state-of-the-art deep neural networks—on written cross-linguistic corpus data covering 1293 different languages and statistically estimate learning difficulty. Using a variety of quantitative methods and machine learning techniques to account for phylogenetic relatedness and geographical proximity of languages, we show that there is robust evidence for a relationship between learning difficulty and speaker population size. However, contrary to expectations derived from previous research, our results suggest that languages with more speakers tend to be harder to learn. Nature Publishing Group UK 2023-10-28 /pmc/articles/PMC10613286/ /pubmed/37898699 http://dx.doi.org/10.1038/s41598-023-45373-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Koplenig, Alexander
Wolfer, Sascha
Languages with more speakers tend to be harder to (machine-)learn
title Languages with more speakers tend to be harder to (machine-)learn
title_full Languages with more speakers tend to be harder to (machine-)learn
title_fullStr Languages with more speakers tend to be harder to (machine-)learn
title_full_unstemmed Languages with more speakers tend to be harder to (machine-)learn
title_short Languages with more speakers tend to be harder to (machine-)learn
title_sort languages with more speakers tend to be harder to (machine-)learn
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10613286/
https://www.ncbi.nlm.nih.gov/pubmed/37898699
http://dx.doi.org/10.1038/s41598-023-45373-z
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