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Large labeled training sets are the critical building blocks of supervisedlearning methods and are key enablers of deep learning techniques. For someapplications, creating labeled training sets is the most time-consuming andexpensive part of applying machine learning. We therefore propose a paradigmfor the programmatic creation of training sets called data programming in whichusers express weak supervision strategies or domain heuristics as labelingfunctions, which are programs that label subsets of the data, but that arenoisy and may conflict. We show that by explicitly representing this trainingset labeling process as a generative model, we can "denoise" the generatedtraining set, and establish theoretically that we can recover the parameters ofthese generative models in a handful of settings. We then show how to modify adiscriminative loss function to make it noise-aware, and demonstrate our methodover a range of discriminative models including logistic regression and LSTMs.Experimentally, on the 2014 TAC-KBP Slot Filling challenge, we show that dataprogramming would have led to a new winning score, and also show that applyingdata programming to an LSTM model leads to a TAC-KBP score almost 6 F1 pointsover a state-of-the-art LSTM baseline (and into second place in thecompetition). Additionally, in initial user studies we observed that dataprogramming may be an easier way for non-experts to create machine learningmodels when training data is limited or unavailable.

Data Programming: Creating Large Training Sets, Quickly