Crowdsourced Labeling for Worker-Task Specialization Model
This work addresses the challenge of efficiently and accurately labeling tasks in crowdsourcing systems, which is incremental as it builds on existing models to improve query efficiency.
The paper tackles the problem of crowdsourced labeling under a worker-task specialization model by designing an inference algorithm that recovers binary task labels with any targeted accuracy, achieving the best known performance in terms of minimum queries per task.
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).