  Scarcity of labeled data is a bottleneck for supervised learning models. A
paradigm that has evolved for dealing with this problem is data programming. An
existing data programming paradigm allows human supervision to be provided as a
set of discrete labeling functions (LF) that output possibly noisy labels to
input instances and a generative modelfor consolidating the weak labels. We
enhance and generalize this paradigm by supporting functions that output a
continuous score (instead of a hard label) that noisily correlates with labels.
We show across five applications that continuous LFs are more natural to
program and lead to improved recall. We also show that accuracy of existing
generative models is unstable with respect to initialization, training epochs,
and learning rates. We give control to the data programmer to guide the
training process by providing intuitive quality guides with each LF. We propose
an elegant method of incorporating these guides into the generative model. Our
overall method, called CAGE, makes the data programming paradigm more reliable
than other tricks based on initialization, sign-penalties, or soft-accuracy
constraints.
