  Finding relevant and high-quality datasets to train machine learning models
is a major bottleneck for practitioners. Furthermore, to address ambitious
real-world use-cases there is usually the requirement that the data come
labelled with high-quality annotations that can facilitate the training of a
supervised model. Manually labelling data with high-quality labels is generally
a time-consuming and challenging task and often this turns out to be the
bottleneck in a machine learning project. Weak Supervised Learning (WSL)
approaches have been developed to alleviate the annotation burden by offering
an automatic way of assigning approximate labels (pseudo-labels) to unlabelled
data based on heuristics, distant supervision and knowledge bases. We apply
probabilistic generative latent variable models (PLVMs), trained on heuristic
labelling representations of the original dataset, as an accurate, fast and
cost-effective way to generate pseudo-labels. We show that the PLVMs achieve
state-of-the-art performance across four datasets. For example, they achieve
22% points higher F1 score than Snorkel in the class-imbalanced Spouse dataset.
PLVMs are plug-and-playable and are a drop-in replacement to existing WSL
frameworks (e.g. Snorkel) or they can be used as benchmark models for more
complicated algorithms, giving practitioners a compelling accuracy boost.
