Near-Optimally Teaching the Crowd to Classify
This work addresses the challenge of optimizing training for crowd workers in labeling tasks, with potential applications in data-driven online education, though it is incremental in improving existing teaching methods.
The paper tackles the problem of efficiently teaching classification rules to learners, such as crowd workers, by proposing STRICT, an algorithm that selects training examples to maximize learning. Experiments on simulated and real image annotation tasks demonstrate its effectiveness, with proven competitive performance against optimal policies and exponential error reduction for linear separators.
How should we present training examples to learners to teach them classification rules? This is a natural problem when training workers for crowdsourcing labeling tasks, and is also motivated by challenges in data-driven online education. We propose a natural stochastic model of the learners, modeling them as randomly switching among hypotheses based on observed feedback. We then develop STRICT, an efficient algorithm for selecting examples to teach to workers. Our solution greedily maximizes a submodular surrogate objective function in order to select examples to show to the learners. We prove that our strategy is competitive with the optimal teaching policy. Moreover, for the special case of linear separators, we prove that an exponential reduction in error probability can be achieved. Our experiments on simulated workers as well as three real image annotation tasks on Amazon Mechanical Turk show the effectiveness of our teaching algorithm.