论文标题
Worker任务专业模型的众包标签
Crowdsourced Labeling for Worker-Task Specialization Model
论文作者
论文摘要
我们考虑在$ d $ type Worker任务专业模型下进行众包标签，其中每个工人和任务都与有限类型的一种特定类型相关联，而工人为匹配类型的任务提供了更可靠的答案，而不是对无与伦比类型的任务。我们设计了一种推理算法，该算法通过使用工人集群，工人技能估计和加权多数投票来恢复二进制任务标签（至任何给定的恢复精度）。设计的推理算法不需要有关工人/任务类型的任何信息，并以最著名的性能（每个任务的最小查询数量）实现了任何有针对性的恢复精度。
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).
