  Due to the unreliability of Internet workers, it's difficult to complete a
crowdsourcing project satisfactorily, especially when the tasks are multiple
and the budget is limited. Recently, meta learning has brought new vitality to
few-shot learning, making it possible to obtain a classifier with a fair
performance using only a few training samples. Here we introduce the concept of
\emph{meta-worker}, a machine annotator trained by meta learning for types of
tasks (i.e., image classification) that are well-fit for AI. Unlike regular
crowd workers, meta-workers can be reliable, stable, and more importantly,
tireless and free. We first cluster unlabeled data and ask crowd workers to
repeatedly annotate the instances nearby the cluster centers; we then leverage
the annotated data and meta-training datasets to build a cluster of
meta-workers using different meta learning algorithms. Subsequently,
meta-workers are asked to annotate the remaining crowdsourced tasks. The
Jensen-Shannon divergence is used to measure the disagreement among the
annotations provided by the meta-workers, which determines whether or not crowd
workers should be invited for further annotation of the same task. Finally, we
model meta-workers' preferences and compute the consensus annotation by
weighted majority voting. Our empirical study confirms that, by combining
machine and human intelligence, we can accomplish a crowdsourcing project with
a lower budget than state-of-the-art task assignment methods, while achieving a
superior or comparable quality.
