2,717 research outputs found
Budget-Optimal Task Allocation for Reliable Crowdsourcing Systems
Crowdsourcing systems, in which numerous tasks are electronically distributed
to numerous "information piece-workers", have emerged as an effective paradigm
for human-powered solving of large scale problems in domains such as image
classification, data entry, optical character recognition, recommendation, and
proofreading. Because these low-paid workers can be unreliable, nearly all such
systems must devise schemes to increase confidence in their answers, typically
by assigning each task multiple times and combining the answers in an
appropriate manner, e.g. majority voting.
In this paper, we consider a general model of such crowdsourcing tasks and
pose the problem of minimizing the total price (i.e., number of task
assignments) that must be paid to achieve a target overall reliability. We give
a new algorithm for deciding which tasks to assign to which workers and for
inferring correct answers from the workers' answers. We show that our
algorithm, inspired by belief propagation and low-rank matrix approximation,
significantly outperforms majority voting and, in fact, is optimal through
comparison to an oracle that knows the reliability of every worker. Further, we
compare our approach with a more general class of algorithms which can
dynamically assign tasks. By adaptively deciding which questions to ask to the
next arriving worker, one might hope to reduce uncertainty more efficiently. We
show that, perhaps surprisingly, the minimum price necessary to achieve a
target reliability scales in the same manner under both adaptive and
non-adaptive scenarios. Hence, our non-adaptive approach is order-optimal under
both scenarios. This strongly relies on the fact that workers are fleeting and
can not be exploited. Therefore, architecturally, our results suggest that
building a reliable worker-reputation system is essential to fully harnessing
the potential of adaptive designs.Comment: 38 pages, 4 figur
Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
We consider a task assignment problem in crowdsourcing, which is aimed at
collecting as many reliable labels as possible within a limited budget. A
challenge in this scenario is how to cope with the diversity of tasks and the
task-dependent reliability of workers, e.g., a worker may be good at
recognizing the name of sports teams, but not be familiar with cosmetics
brands. We refer to this practical setting as heterogeneous crowdsourcing. In
this paper, we propose a contextual bandit formulation for task assignment in
heterogeneous crowdsourcing, which is able to deal with the
exploration-exploitation trade-off in worker selection. We also theoretically
investigate the regret bounds for the proposed method, and demonstrate its
practical usefulness experimentally
Crowdsourcing complex workflows under budget constraints
We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of interdependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then determines the number of inter-dependent micro-tasks and the price to pay for each task within each workflow, given the corresponding budget constraints. We empirically evaluate it on a well-known crowdsourcing-based text correction workflow using Amazon Mechanical Turk, and show that Budgeteer can achieve similar levels of accuracy to current benchmarks, but is on average 45% cheaper
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