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Easy, Reproducible and Quality-Controlled Data Collection with Crowdaq [article]

Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, Ana Marasovic, Zhen Nie
2020 arXiv   pre-print
High-quality and large-scale data are key to success for AI systems.  ...  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  ...  for data collection reproducibility.  ... 
arXiv:2010.06694v1 fatcat:5jnkjtuz4vehzea7dmntepwnja

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
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations   unpublished
High-quality and large-scale data are key to success for AI systems.  ...  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.  ...  CROWDAQ is an open-source online platform aimed to reduce this overhead and improve reproducibility via customizable UI components, automated qualification control, and easy-to-reproduce pipelines.  ... 
doi:10.18653/v1/2020.emnlp-demos.17 fatcat:hvibmuxfe5d7nkv22bjcebttni