Who Should Label What? Instance Allocation in Multiple Expert Active Learning

The active learning (AL) framework is an increasingly popular strategy for reducing the amount of human labeling effort required to induce a predictive model. Most work in AL has assumed that a single, infallible oracle provides labels requested by the learner at a fixed cost. However, real-world applications suitable for AL often include multiple domain experts who provide labels of varying cost and quality. We explore this multiple expert active learning (MEAL) scenario and develop a novel algorithm for instance allocation that exploits the meta-cognitive abilities of novice (cheap) experts in order to make the best use of the experienced (expensive) annotators. We demonstrate that this strategy outperforms strong baseline approaches to MEAL on both a sentiment analysis dataset and two datasets from our motivating application of biomedical citation screening. Furthermore, we provide evidence that novice labelers are often aware of which instances they are likely to mislabel.