Exploring Effectiveness of Inter-Microtask Qualification Tests in Crowdsourcing
This research provides insights for crowdsourcing platform requesters on optimizing worker qualification strategies to improve annotation accuracy, particularly for tasks of varying difficulty.
This study explored the effectiveness of using a single qualification test across multiple microtask types on Amazon Mechanical Turk. It found that workers who passed a difficult qualification test achieved better annotation accuracy regardless of the actual task's difficulty, and Master-qualified workers performed better on low-difficulty tasks but not as well as qualified workers on high-difficulty tasks.
Qualification tests in crowdsourcing are often used to pre-filter workers by measuring their ability in executing microtasks.While creating qualification tests for each task type is considered as a common and reasonable way, this study investigates into its worker-filtering performance when the same qualification test is used across multiple types of tasks.On Amazon Mechanical Turk, we tested the annotation accuracy in six different cases where tasks consisted of two different difficulty levels, arising from the identical real-world domain: four combinatory cases in which the qualification test and the actual task were the same or different from each other, as well as two other cases where workers with Masters Qualification were asked to perform the actual task only.The experimental results demonstrated the two following findings: i) Workers that were assigned to a difficult qualification test scored better annotation accuracy regardless of the difficulty of the actual task; ii) Workers with Masters Qualification scored better annotation accuracy on the low-difficulty task, but were not as accurate as those who passed a qualification test on the high-difficulty task.