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The Zeno's Paradox of `Low-Resource' Languages

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arxiv 2410.20817 v1 pith:O2BVQZR3 submitted 2024-10-28 cs.CL

Hellina Hailu Nigatu , Atnafu Lambebo Tonja , Benjamin Rosman , Thamar Solorio , Monojit Choudhury This is my paper · ORCID
classification cs.CL
keywords languagelanguageslow-resourceaxeswhenanalysisanalyzedanthology
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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.

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  1. Pruning for Performance: Efficient Idiom and Metaphor Classification in Low-Resource Konkani Using mBERT

    cs.CL 2025-05 conditional novelty 5.0 of 10

    A pruned mBERT+BiLSTM model reaches 78% accuracy on a new 200-sentence Konkani metaphor test set and 83% on idiom classification, though the test set is very small and no error bars are given.

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