  The absence of labeled data for training neural models is often addressed by
leveraging knowledge about the specific task, resulting in heuristic but noisy
labels. The knowledge is captured in labeling functions, which detect certain
regularities or patterns in the training samples and annotate corresponding
labels for training. This process of weakly supervised training may result in
an over-reliance on the signals captured by the labeling functions and hinder
models to exploit other signals or to generalize well. We propose KnowMAN, an
adversarial scheme that enables to control influence of signals associated with
specific labeling functions. KnowMAN forces the network to learn
representations that are invariant to those signals and to pick up other
signals that are more generally associated with an output label. KnowMAN
strongly improves results compared to direct weakly supervised learning with a
pre-trained transformer language model and a feature-based baseline.
