  Recently, there has been a burst in the number of research projects on human
computation via crowdsourcing. Multiple choice (or labeling) questions could be
referred to as a common type of problem which is solved by this approach. As an
application, crowd labeling is applied to find true labels for large machine
learning datasets. Since crowds are not necessarily experts, the labels they
provide are rather noisy and erroneous. This challenge is usually resolved by
collecting multiple labels for each sample, and then aggregating them to
estimate the true label. Although the mechanism leads to high-quality labels,
it is not actually cost-effective. As a result, efforts are currently made to
maximize the accuracy in estimating true labels, while fixing the number of
acquired labels.
  This paper surveys methods to aggregate redundant crowd labels in order to
estimate unknown true labels. It presents a unified statistical latent model
where the differences among popular methods in the field correspond to
different choices for the parameters of the model. Afterwards, algorithms to
make inference on these models will be surveyed. Moreover, adaptive methods
which iteratively collect labels based on the previously collected labels and
estimated models will be discussed. In addition, this paper compares the
distinguished methods, and provides guidelines for future work required to
address the current open issues.
