Beyond Catalogue Counts: the Dataset Visibility Asymmetry in Low-Resource Multilingual NLP
Multilingual NLP often relies on dataset counts from centralized catalogues to characterize which languages are resource-rich or resource-poor. However, these catalogues record only one layer of dataset visibility: what has been registered or institutionally distributed. They do not necessarily reflect which datasets are created, cited, or reused in the research literature. To examine this gap, we combine a catalogue-based baseline with literature-backed evidence of dataset circulation. We introduce the Resource Density Index (RDI), defined as the number of catalogued datasets per one million speakers, and compute it for the 200 most widely spoken languages in Ethnologue. Among them, 118 ...
| Authors | Zhiyin Tan, Changxu Duan |
|---|---|
| Primary category | cs.CL |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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