  While the NLP community is generally aware of resource disparities among
languages, we lack research that quantifies the extent and types of such
disparity. Prior surveys estimating the availability of resources based on the
number of datasets can be misleading as dataset quality varies: many datasets
are automatically induced or translated from English data. To provide a more
comprehensive picture of language resources, we examine the characteristics of
156 publicly available NLP datasets. We manually annotate how they are created,
including input text and label sources and tools used to build them, and what
they study, tasks they address and motivations for their creation. After
quantifying the qualitative NLP resource gap across languages, we discuss how
to improve data collection in low-resource languages. We survey
language-proficient NLP researchers and crowd workers per language, finding
that their estimated availability correlates with dataset availability. Through
crowdsourcing experiments, we identify strategies for collecting high-quality
multilingual data on the Mechanical Turk platform. We conclude by making macro
and micro-level suggestions to the NLP community and individual researchers for
future multilingual data development.
