Nashaat, MonaGhosh, AindrilaMiller, JamesQuader, Shaikh2020-01-042020-01-042020-01-07978-0-9981331-3-3http://hdl.handle.net/10125/63767Obtaining 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalCollaboration for Data Scienceactive learninghuman-in-the-loopmachine learningsupervised learningweak supervisionConference Paper10.24251/HICSS.2020.028