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

TL;DR

This paper provides a detailed analysis of 156 multilingual NLP datasets, highlighting resource disparities across languages, and offers strategies for improving data collection in low-resource languages.

Contribution

It introduces a comprehensive annotation of dataset creation, quality assessment, and proposes practical strategies for enhancing multilingual data collection.

Findings

Resource disparities are significant across languages.

Crowdsourcing can effectively improve multilingual data quality.

Estimated researcher and crowd worker availability correlates with dataset presence.

Abstract

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.