WeSAL: Applying Active Supervision to Find High-quality Labels at Industrial Scale

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dc.contributor.authorNashaat, Mona
dc.contributor.authorGhosh, Aindrila
dc.contributor.authorMiller, James
dc.contributor.authorQuader, Shaikh
dc.date.accessioned2020-01-04T07:10:47Z
dc.date.available2020-01-04T07:10:47Z
dc.date.issued2020-01-07
dc.description.abstractObtaining hand-labeled training data is one of the most tedious and expensive parts of the machine learning pipeline. Previous approaches, such as active learning aim at optimizing user engagement to acquire accurate labels. Other methods utilize weak supervision to generate low-quality labels at scale. In this paper, we propose a new hybrid method named WeSAL that incorporates Weak Supervision sources with Active Learning to keep humans in the loop. The method aims to generate large-scale training labels while enhancing its quality by involving domain experience. To evaluate WeSAL, we compare it with two-state-of-the-art labeling techniques, Active Learning and Data Programming. The experiments use five publicly available datasets and a real-world dataset of 1.5M records provided by our industrial partner, IBM. The results indicate that WeSAL can generate large-scale, high-quality labels while reducing the labeling cost by up to 68% compared to active learning.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2020.028
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63767
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCollaboration for Data Science
dc.subjectactive learning
dc.subjecthuman-in-the-loop
dc.subjectmachine learning
dc.subjectsupervised learning
dc.subjectweak supervision
dc.titleWeSAL: Applying Active Supervision to Find High-quality Labels at Industrial Scale
dc.typeConference Paper
dc.type.dcmiText

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