  Task selection (picking an appropriate labeling task) and worker selection
(assigning the labeling task to a suitable worker) are two major challenges in
task assignment for crowdsourcing. Recently, worker selection has been
successfully addressed by the bandit-based task assignment (BBTA) method, while
task selection has not been thoroughly investigated yet. In this paper, we
experimentally compare several task selection strategies borrowed from active
learning literature, and show that the least confidence strategy significantly
improves the performance of task assignment in crowdsourcing.
