Zhen Nie


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Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ
Qiang Ning | Hao Wu | Pradeep Dasigi | Dheeru Dua | Matt Gardner | Robert L. Logan IV | Ana Marasović | Zhen Nie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

High-quality and large-scale data are key to success for AI systems. However, large-scale data annotation efforts are often confronted with a set of common challenges: (1) designing a user-friendly annotation interface; (2) training enough annotators efficiently; and (3) reproducibility. To address these problems, we introduce CROWDAQ, an open-source platform that standardizes the data collection pipeline with customizable user-interface components, automated annotator qualification, and saved pipelines in a re-usable format. We show that CROWDAQ simplifies data annotation significantly on a diverse set of data collection use cases and we hope it will be a convenient tool for the community.