Sequential crowdsourced labeling as an epsilon-greedy exploration in a Markov Decision Process

Abstract

Crowdsourcing marketplaces are widely used for curating large annotated datasets by collecting labels from multiple annotators. In such scenarios one has to balance the tradeoff between the accuracy of the collected labels, the cost of acquiring these labels, and the time taken to finish the labeling task. With the goal of reducing the labeling cost, we introduce the notion of sequential crowd-sourced labeling, where instead of asking for all the labels in one shot we acquire labels from annotators sequentially one at a time. We model it as an epsilon-greedy exploration in a Markov Decision Process with a Bayesian decision theoretic utility function that incorporates accuracy, cost and time. Experimental results confirm that the proposed sequential labeling procedure can achieve similar accuracy at roughly half the labeling cost and at any stage in the labeling process the algorithm achieves a higher accuracy compared to randomly asking for the next label.