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We propose a fully Bayesian framework for learning ground truth labels fromnoisy annotators.  Our framework ensures scalability by factoring a generative, Bayesian softclustering model over label distributions into the classic David and Skenejoint annotator-data model. Earlier research along these lines has neitherfully incorporated label distributions nor explored clustering by annotatorsonly or data only. Our framework incorporates all of these properties as:  (1) a graphical model designed to provide better ground truth estimates ofannotator responses as input to \emph{any} black box supervised learningalgorithm, and  (2) a standalone neural model whose internal structure captures many of theproperties of the graphical model.  We conduct supervised learning experiments using both models and compare themto the performance of one baseline and a state-of-the-art model.

Improving Label Quality by Jointly Modeling Items and Annotators