  Creating large, good quality labeled data has become one of the major
bottlenecks for developing machine learning applications. Multiple techniques
have been developed to either decrease the dependence of labeled data
(zero/few-shot learning, weak supervision) or to improve the efficiency of
labeling process (active learning). Among those, Weak Supervision has been
shown to reduce labeling costs by employing hand crafted labeling functions
designed by domain experts. We propose AutoWS -- a novel framework for
increasing the efficiency of weak supervision process while decreasing the
dependency on domain experts. Our method requires a small set of labeled
examples per label class and automatically creates a set of labeling functions
to assign noisy labels to numerous unlabeled data. Noisy labels can then be
aggregated into probabilistic labels used by a downstream discriminative
classifier. Our framework is fully automatic and requires no hyper-parameter
specification by users. We compare our approach with different state-of-the-art
work on weak supervision and noisy training. Experimental results show that our
method outperforms competitive baselines.
