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Programmatic weak supervision creates models without hand-labeled trainingdata by combining the outputs of heuristic labelers. Existing frameworks makethe restrictive assumption that labelers output a single class label. Enablingusers to create partial labelers that output subsets of possible class labelswould greatly expand the expressivity of programmatic weak supervision. Weintroduce this capability by defining a probabilistic generative model that canestimate the underlying accuracies of multiple noisy partial labelers withoutground truth labels. We show how to scale up learning, for example learning on100k examples in one minute, a 300x speed up compared to a naiveimplementation. We also prove that this class of models is genericallyidentifiable up to label swapping under mild conditions. We evaluate ourframework on three text classification and six object classification tasks. Ontext tasks, adding partial labels increases average accuracy by 8.6 percentagepoints. On image tasks, we show that partial labels allow us to approach somezero-shot object classification problems with programmatic weak supervision byusing class attributes as partial labelers. On these tasks, our framework hasaccuracy comparable to recent embedding-based zero-shot learning methods, whileusing only pre-trained attribute detectors.

Learning from Multiple Noisy Partial Labelers