Crowdsourcing provides a new way to distribute enormous tasks to a crowd of annotators. The divergent knowledge background and personal preferences of crowd annotators lead to noisy (or even inconsistent) answers to a same question. However, diverse labels provide us information about the underlying structures of tasks and annotators. This paper proposes latent-class assumptions for learning-from-crowds models, that is, items can be separated into several latent classes and workers' annotating behaviors may differ among different classes. We propose a nonparametric model to uncover the latent classes, and also extend the state-of-the-art minimax entropy estimator to learn latent structures. Experimental results on both synthetic data and real data collected from Amazon Mechanical Turk demonstrate our methods can disclose interesting and meaningful latent structures, and incorporating latent class structures can also bring significant improvements on ground truth label recovery for difficult tasks.
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