Crowdsourced Labeling for Worker-Task Specialization Block Model

We consider crowdsourced labeling under a worker-task specialization block model, where each worker and task is associated with one particular type among a finite set of types and a worker provides a more reliable answer to tasks of the matched type than to the tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering and weighted majority voting. The designed inference algorithm does not require any information about worker types, task types as well as worker reliability parameters, and achieve any targeted recovery accuracy with the best known performance (minimum number of queries per task) for any parameter regimes.