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The scalability of the labeling process and the attainable quality of labelshave become limiting factors for many applications of machine learning. Theprogrammatic creation of labeled datasets via the synthesis of noisy heuristicsprovides a promising avenue to address this problem. We propose to improvemodeling of latent class variables in the programmatic creation of labeleddatasets by incorporating pairwise feedback into the process. We discuss theease with which such pairwise feedback can be obtained or generated in manyapplication domains. Our experiments show that even a small number of sourcesof pairwise feedback can substantially improve the quality of the posteriorestimate of the latent class variable.

Pairwise Feedback for Data Programming