The absence of labeled data for training neu- ral 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 in- fluence of signals associated with specific la- beling functions. KnowMAN forces the net- work to learn representations that are invari- ant to those signals and to pick up other sig- nals that are more generally associated with an output label. KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer lan- guage model and a feature-based baseline

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