  Obtaining large annotated datasets is critical for training successful
machine learning models and it is often a bottleneck in practice. Weak
supervision offers a promising alternative for producing labeled datasets
without ground truth annotations by generating probabilistic labels using
multiple noisy heuristics. This process can scale to large datasets and has
demonstrated state of the art performance in diverse domains such as healthcare
and e-commerce. One practical issue with learning from user-generated
heuristics is that their creation requires creativity, foresight, and domain
expertise from those who hand-craft them, a process which can be tedious and
subjective. We develop the first framework for interactive weak supervision in
which a method proposes heuristics and learns from user feedback given on each
proposed heuristic. Our experiments demonstrate that only a small number of
feedback iterations are needed to train models that achieve highly competitive
test set performance without access to ground truth training labels. We conduct
user studies, which show that users are able to effectively provide feedback on
heuristics and that test set results track the performance of simulated
oracles.
