AtlasNLP: A Country-Aware Atlas of Dataset Representation in NLP

arXiv:2608.30107 · cs.CL, cs.AI, cs.CY · Submitted 2026-08-31 · Read on arXiv

cs.CL, cs.AI, cs.CY

Submitted: 2026-08-31

Updated: 2026-09-08

Comments: Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing

License: http://creativecommons.org/licenses/by/4.0/

The gist: Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy.

Terminology

Abstract

Understanding which countries are represented in NLP datasets is essential for identifying gaps, targeting data collection, measuring progress, and informing AI policy. However, geographic metadata is very rarely available, and country-level representation is often hidden behind broad language-level claims. We introduce AtlasNLP, a country-aware atlas of over 13,000 NLP dataset records across normalized NLP task categories, tracking both the populations represented and where datasets are produced. AtlasNLP includes AtlasNLP-Gold, a human-curated reference set, and AtlasNLP-Core, an ACL-derived large-scale collection. Using this resource, we show that (1) dataset coverage is highly uneven across countries and tasks; (2) dataset production and representation are geographically asymmetric; and (3) language coverage does not imply geographic representation. These findings reveal blind spots in current dataset documentation practices and motivate more explicit geographic metadata for country-aware NLP evaluation.

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