Statistical Decision Making for Optimal Budget Allocation in Crowd Labeling

TL;DR

This paper develops an optimal budget allocation strategy for crowd labeling tasks using a Bayesian MDP framework, introducing an efficient approximate policy that improves labeling accuracy over existing methods.

Contribution

It formulates the budget allocation as a Bayesian MDP and proposes a computationally efficient optimistic knowledge gradient policy for better accuracy.

Findings

The proposed policy outperforms existing methods in simulated data.

It achieves higher labeling accuracy at the same budget level.

Applicable to both homogeneous and heterogeneous worker marketplaces.

Abstract

In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different reliability, it is desirable to have an optimal policy to allocate the budget among all instance-worker pairs such that the overall labeling accuracy is maximized. We consider categorical labeling tasks and formulate the budget allocation problem as a Bayesian Markov decision process (MDP), which simultaneously conducts learning and decision making. Using the dynamic programming (DP) recurrence, one can obtain the optimal allocation policy. However, DP quickly becomes computationally intractable when the size of the problem increases. To solve this challenge, we propose a computationally efficient approximate policy, called optimistic knowledge gradient policy. Our MDP is a quite general framework, which applies to both pull crowdsourcing marketplaces with homogeneous workers and push marketplaces with heterogeneous workers. It can also incorporate the contextual information of instances when they are available. The experiments on both simulated and real data show that the proposed policy achieves a higher labeling accuracy than other existing policies at the same budget level.