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Weakly supervised learning is a popular approach for training machinelearning models in low-resource settings. Instead of requesting high-qualityyet costly human annotations, it allows training models with noisy annotationsobtained from various weak sources. Recently, many sophisticated approacheshave been proposed for robust training under label noise, reporting impressiveresults. In this paper, we revisit the setup of these approaches and find thatthe benefits brought by these approaches are significantly overestimated.Specifically, we find that the success of existing weakly supervised learningapproaches heavily relies on the availability of clean validation sampleswhich, as we show, can be leveraged much more efficiently by simply training onthem. After using these clean labels in training, the advantages of using thesesophisticated approaches are mostly wiped out. This remains true even whenreducing the size of the available clean data to just five samples per class,making these approaches impractical. To understand the true value of weaklysupervised learning, we thoroughly analyze diverse NLP datasets and tasks toascertain when and why weakly supervised approaches work. Based on ourfindings, we provide recommendations for future research.

Weaker Than You Think: A Critical Look at Weakly Supervised Learning