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

2026-08-30

AI Summary

研究通过构建AtlasNLP解决NLP数据集中国家代表性信息缺失问题，采用人工整理与大规模自动收集相结合的方法，揭示了数据集覆盖的不均衡性和地理不对称性。

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.

Problem

• NLP datasets
• geographic metadata
• country representation

Innovation

• country-aware
• NLP datasets
• geographic metadata
• dataset representation