  State-of-the-art deep neural networks require large-scale labeled training
data that is often expensive to obtain or not available for many tasks. Weak
supervision in the form of domain-specific rules has been shown to be useful in
such settings to automatically generate weakly labeled training data. However,
learning with weak rules is challenging due to their inherent heuristic and
noisy nature. An additional challenge is rule coverage and overlap, where prior
work on weak supervision only considers instances that are covered by weak
rules, thus leaving valuable unlabeled data behind.
  In this work, we develop a weak supervision framework (ASTRA) that leverages
all the available data for a given task. To this end, we leverage task-specific
unlabeled data through self-training with a model (student) that considers
contextualized representations and predicts pseudo-labels for instances that
may not be covered by weak rules. We further develop a rule attention network
(teacher) that learns how to aggregate student pseudo-labels with weak rule
labels, conditioned on their fidelity and the underlying context of an
instance. Finally, we construct a semi-supervised learning objective for
end-to-end training with unlabeled data, domain-specific rules, and a small
amount of labeled data. Extensive experiments on six benchmark datasets for
text classification demonstrate the effectiveness of our approach with
significant improvements over state-of-the-art baselines.
