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Weak supervision (WS) is an alternative to the traditional supervisedlearning to address the need for ground truth. Data programming is a practicalWS approach that allows programmatic labeling data samples using labelingfunctions (LFs) instead of hand-labeling each data point. However, the existingapproach fails to fully exploit the domain knowledge encoded into LFs,especially when the LFs' coverage is low. This is due to the common dataprogramming pipeline that neglects to utilize data features during thegenerative process. This paper proposes a new approach called reinforcedlabeling (RL). Given an unlabeled dataset and a set of LFs, RL augments theLFs' outputs to cases not covered by LFs based on similarities among samples.Thus, RL can lead to higher labeling coverage for training an end classifier.The experiments on several domains (classification of YouTube comments, winequality, and weather prediction) result in considerable gains. The new approachproduces significant performance improvement, leading up to +21 points inaccuracy and +61 points in F1 scores compared to the state-of-the-art dataprogramming approach.

Label Augmentation with Reinforced Labeling for Weak Supervision