Efficient crowdsourcing of crowd-generated microtasks
This addresses the challenge for crowdsourcers in handling dynamic task sets, with potential applications in generating diverse training data for machine learning and improving user-generated content platforms, though it appears incremental as it builds on existing efficient crowdsourcing algorithms.
The paper tackles the problem of efficiently managing a growing set of crowd-generated microtasks by introducing cost forecasting, which balances resources between eliciting new tasks and receiving responses to existing ones, leading to improved accuracy in experiments with real and synthetic data.
Allowing members of the crowd to propose novel microtasks for one another is an effective way to combine the efficiencies of traditional microtask work with the inventiveness and hypothesis generation potential of human workers. However, microtask proposal leads to a growing set of tasks that may overwhelm limited crowdsourcer resources. Crowdsourcers can employ methods to utilize their resources efficiently, but algorithmic approaches to efficient crowdsourcing generally require a fixed task set of known size. In this paper, we introduce *cost forecasting* as a means for a crowdsourcer to use efficient crowdsourcing algorithms with a growing set of microtasks. Cost forecasting allows the crowdsourcer to decide between eliciting new tasks from the crowd or receiving responses to existing tasks based on whether or not new tasks will cost less to complete than existing tasks, efficiently balancing resources as crowdsourcing occurs. Experiments with real and synthetic crowdsourcing data show that cost forecasting leads to improved accuracy. Accuracy and efficiency gains for crowd-generated microtasks hold the promise to further leverage the creativity and wisdom of the crowd, with applications such as generating more informative and diverse training data for machine learning applications and improving the performance of user-generated content and question-answering platforms.