  In the weakly supervised learning paradigm, labeling functions automatically
assign heuristic, often noisy, labels to data samples. In this work, we provide
a method for learning from weak labels by separating two types of complementary
information associated with the labeling functions: information related to the
target label and information specific to one labeling function only. Both types
of information are reflected to different degrees by all labeled instances. In
contrast to previous works that aimed at correcting or removing wrongly labeled
instances, we learn a branched deep model that uses all data as-is, but splits
the labeling function information in the latent space. Specifically, we propose
the end-to-end model SepLL which extends a transformer classifier by
introducing a latent space for labeling function specific and task-specific
information. The learning signal is only given by the labeling functions
matches, no pre-processing or label model is required for our method. Notably,
the task prediction is made from the latent layer without any direct task
signal. Experiments on Wrench text classification tasks show that our model is
competitive with the state-of-the-art, and yields a new best average
performance.
