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Technical Program

Paper Detail

Paper IDD7-S4-T3.3
Paper Title Crowdsourced Labeling for Worker-Task Specialization Model
Authors Doyeon Kim, Hye Won Chung, KAIST, Korea (South)
Session D7-S4-T3: Learning & Side Information
Chaired Session: Tuesday, 20 July, 23:00 - 23:20
Engagement Session: Tuesday, 20 July, 23:20 - 23:40
Abstract 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).