Adaptive task assignment for crowdsourced classification

Crowdsourcing markets have gained popularity as a tool for inexpensively collecting data from diverse populations of workers. Classification tasks, in which workers provide labels (such as "offensive" or "not offensive") for instances (such as "websites"), are among the most common tasks posted, but due to human error and the prevalence of spam, the labels collected are often noisy. This problem is typically addressed by collecting labels for each instance from multiple workers and combining them in a clever way, but the question of how to choose which tasks to assign to each worker is often overlooked. We investigate the problem of task assignment and label inference for heterogeneous classification tasks. By applying online primal-dual techniques, we derive a provably near-optimal adaptive assignment algorithm. We show that adaptively assigning workers to tasks can lead to more accurate predictions at a lower cost when the available workers are diverse.