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In the weakly supervised learning paradigm, labeling functions automaticallyassign heuristic, often noisy, labels to data samples. In this work, we providea method for learning from weak labels by separating two types of complementaryinformation associated with the labeling functions: information related to thetarget label and information specific to one labeling function only. Both typesof information are reflected to different degrees by all labeled instances. Incontrast to previous works that aimed at correcting or removing wrongly labeledinstances, we learn a branched deep model that uses all data as-is, but splitsthe labeling function information in the latent space. Specifically, we proposethe end-to-end model SepLL which extends a transformer classifier byintroducing a latent space for labeling function specific and task-specificinformation. The learning signal is only given by the labeling functionsmatches, no pre-processing or label model is required for our method. Notably,the task prediction is made from the latent layer without any direct tasksignal. Experiments on Wrench text classification tasks show that our model iscompetitive with the state-of-the-art, and yields a new best averageperformance.

SepLL: Separating Latent Class Labels from Weak Supervision Noise