Iterative Bayesian Learning for Crowdsourced Regression
This addresses the challenge of improving accuracy in crowdsourced regression for platforms relying on low-paid workers, though it is incremental as it builds on existing methods for worker quality estimation.
The paper tackles the problem of learning heterogeneous worker quality in crowdsourced regression tasks, where continuous labels make identifying good or bad workers non-trivial, and introduces a Bayesian iterative scheme that provably achieves optimal mean squared error, with evaluations on synthetic and real-world datasets supporting these results.
Crowdsourcing platforms emerged as popular venues for purchasing human intelligence at low cost for large volume of tasks. As many low-paid workers are prone to give noisy answers, a common practice is to add redundancy by assigning multiple workers to each task and then simply average out these answers. However, to fully harness the wisdom of the crowd, one needs to learn the heterogeneous quality of each worker. We resolve this fundamental challenge in crowdsourced regression tasks, i.e., the answer takes continuous labels, where identifying good or bad workers becomes much more non-trivial compared to a classification setting of discrete labels. In particular, we introduce a Bayesian iterative scheme and show that it provably achieves the optimal mean squared error. Our evaluations on synthetic and real-world datasets support our theoretical results and show the superiority of the proposed scheme.