Author: Ning, Qiang; Wu, Hao; Dasigi, Pradeep; Dua, Dheeru; Gardner, Matt; RobertL.Logan, IV; Marasovic, Ana; Nie, Zhen
Title: Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq Cord-id: m9qcxjxd Document date: 2020_10_6
ID: m9qcxjxd
Snippet: 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 p
Document: 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.
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