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        <title>The Zeno’s Paradox of ‘Low-Resource’ Languages</title>
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    <name type="personal">
        <namePart type="given">Hellina</namePart>
        <namePart type="given">Hailu</namePart>
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    <name type="personal">
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        <namePart type="family">Tonja</namePart>
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    <name type="personal">
        <namePart type="given">Monojit</namePart>
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            <title>Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Mohit</namePart>
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            <namePart type="given">Yun-Nung</namePart>
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    <abstract>The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a ‘low-resource language.’ To understand how NLP papers define and study ‘low resource’ languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword ‘low-resource.’ Based on our analysis, we show how several interacting axes contribute to ‘low-resourcedness’ of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.</abstract>
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%0 Conference Proceedings
%T The Zeno’s Paradox of ‘Low-Resource’ Languages
%A Nigatu, Hellina Hailu
%A Tonja, Atnafu
%A Rosman, Benjamin
%A Solorio, Thamar
%A Choudhury, Monojit
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F nigatu-etal-2024-zenos
%X The disparity in the languages commonly studied in Natural Language Processing (NLP) is typically reflected by referring to languages as low vs high-resourced. However, there is limited consensus on what exactly qualifies as a ‘low-resource language.’ To understand how NLP papers define and study ‘low resource’ languages, we qualitatively analyzed 150 papers from the ACL Anthology and popular speech-processing conferences that mention the keyword ‘low-resource.’ Based on our analysis, we show how several interacting axes contribute to ‘low-resourcedness’ of a language and why that makes it difficult to track progress for each individual language. We hope our work (1) elicits explicit definitions of the terminology when it is used in papers and (2) provides grounding for the different axes to consider when connoting a language as low-resource.
%U https://aclanthology.org/2024.emnlp-main.983
%P 17753-17774

Markdown (Informal)

[The Zeno’s Paradox of ‘Low-Resource’ Languages](https://aclanthology.org/2024.emnlp-main.983) (Nigatu et al., EMNLP 2024)

• The Zeno’s Paradox of ‘Low-Resource’ Languages (Nigatu et al., EMNLP 2024)

ACL

• Hellina Hailu Nigatu, Atnafu Tonja, Benjamin Rosman, Thamar Solorio, and Monojit Choudhury. 2024. The Zeno’s Paradox of ‘Low-Resource’ Languages. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17753–17774, Miami, Florida, USA. Association for Computational Linguistics.