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.

本文考虑众包任务分配中的可靠性问题，提出了一种基于置信传播和低秩矩阵逼近的算法来决定如何分配任务，并从工人的答案中推断出正确答案。比较了不同算法的表现，发现基于者非自适应的算法是最优的。此外，还强调了建立可靠的工人信誉系统对于完全运用自适应设计的潜力至关重要。