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Obtaining large annotated datasets is critical for training successfulmachine learning models and it is often a bottleneck in practice. Weaksupervision offers a promising alternative for producing labeled datasetswithout ground truth annotations by generating probabilistic labels usingmultiple noisy heuristics. This process can scale to large datasets and hasdemonstrated state of the art performance in diverse domains such as healthcareand e-commerce. One practical issue with learning from user-generatedheuristics is that their creation requires creativity, foresight, and domainexpertise from those who hand-craft them, a process which can be tedious andsubjective. We develop the first framework for interactive weak supervision inwhich a method proposes heuristics and learns from user feedback given on eachproposed heuristic. Our experiments demonstrate that only a small number offeedback iterations are needed to train models that achieve highly competitivetest set performance without access to ground truth training labels. We conductuser studies, which show that users are able to effectively provide feedback onheuristics and that test set results track the performance of simulatedoracles.

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling