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While the NLP community is generally aware of resource disparities amonglanguages, we lack research that quantifies the extent and types of suchdisparity. Prior surveys estimating the availability of resources based on thenumber of datasets can be misleading as dataset quality varies: many datasetsare automatically induced or translated from English data. To provide a morecomprehensive picture of language resources, we examine the characteristics of156 publicly available NLP datasets. We manually annotate how they are created,including input text and label sources and tools used to build them, and whatthey study, tasks they address and motivations for their creation. Afterquantifying the qualitative NLP resource gap across languages, we discuss howto improve data collection in low-resource languages. We surveylanguage-proficient NLP researchers and crowd workers per language, findingthat their estimated availability correlates with dataset availability. Throughcrowdsourcing experiments, we identify strategies for collecting high-qualitymultilingual data on the Mechanical Turk platform. We conclude by making macroand micro-level suggestions to the NLP community and individual researchers forfuture multilingual data development.

Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources