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Beyond Catalogue Counts: The Dataset Visibility Asymmetry in Low-Resource Multilingual NLP

Summary

Surveys of multilingual NLP often decide which languages are "low-resource" by counting entries in central catalogues. Tan and Duan argue that such counts capture only what was registered or distributed through institutions, not what researchers actually build, cite and reuse. They define the Resource Density Index (RDI), the number of catalogued datasets per million speakers. They compute it from LRE Map and LDC entries for the 200 most-spoken languages in the 2025 Ethnologue list. Most of these languages have almost no catalogue presence. For 141 low-visibility languages, the authors then mined the Semantic Scholar corpus for dataset citations using an LLM-assisted pipeline adapted from their earlier dataset-discovery work. The results were manually validated, deduplicated and annotated for accessibility. This uncovered hundreds of datasets that catalogues miss, though many are no longer reachable online. The study suggests that part of the apparent data scarcity comes from poor documentation, discoverability and preservation, not from datasets never being created. This matters for how benchmarks, shared tasks and claims about language inequality are framed.

Key points

  • RDI (catalogued datasets per 1M speakers) was computed for 200 languages. Of these, 118 (59%) score a mean RDI of zero when the LRE Map and LDC are averaged together, and another 23 fall below 0.1.
  • Citation mining over the Semantic Scholar corpus for these 141 languages produced 609 validated unique datasets covering 53 languages.
  • Only 356 of the 609 datasets still have working public links, so documented existence and present-day open access diverge.
  • Indonesian shows the gap clearly: the LRE Map lists 31 resources, NusaCrowd reports 137, and the literature audit finds 196. For Marathi, the catalogue shows 14 and the validated inventory 41.
  • Code and data are available at github.com/zhiyintan/dataset-visibility-asymmetry (LREC 2026, pp. 6068–6079).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

Fields: Linguistics & languages

Cite

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.63317/3bep4yiomtp2

@inproceedings{tan2026beyond,
  title = {{Beyond Catalogue Counts: The Dataset Visibility Asymmetry in Low-Resource Multilingual NLP}},
  author = {Tan, Zhiyin and Duan, Changxu},
  booktitle = {Proceedings of the Fifteenth Language Resources and Evaluation Conference},
  year = {2026},
  doi = {10.63317/3bep4yiomtp2},
  url = {https://doi.org/10.63317/3bep4yiomtp2}
}

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