We consider crowdsourced labeling under a $d$-type worker-task specialization 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 tasks of unmatched types. We design an inference algorithm that recovers binary task labels (up to any given recovery accuracy) by using worker clustering, worker skill estimation and weighted majority voting. The designed inference algorithm does not require any information about worker/task types, and achieves any targeted recovery accuracy with the best known performance (minimum number of queries per task).

翻译：我们考虑以美元型工人-工作任务专业化模式为众包标签,即每个工人和任务都与一定类型中的一种特定类型挂钩,而工人对匹配类型的任务比不匹配类型的任务提供更可靠的答案。 我们设计一种推论算法,通过工人集群、工人技能估计和加权多数投票恢复二元任务标签(直至任何一定的回收准确性 ) 。 设计的推论算法并不要求任何关于工人/工作类型的信息,并且以已知的最佳性能(每个任务至少查询次数)实现任何有针对性的回收准确性。