The Zeno’s Paradox of ‘Low-Resource’ Languages
Mohamed bin Zayed University of Artificial Intelligence and Electrical Engineering & Computer Science Department, University of California, Berkeley · Mohamed bin Zayed University of Artificial Intelligence and Instituto Politécnico Nacional · University of the Witwatersrand · Mohamed bin Zayed University of Artificial Intelligence and University of Houston · Mohamed bin Zayed University of Artificial Intelligence
PDF 由论文原始站点提供，PaperCompass 不保存论文文件。DOI 10.18653/v1/2024.emnlp-main.983 ↗
摘要
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.