  Quality improvement methods are essential to gathering high-quality
crowdsourced data, both for research and industry applications. A popular and
broadly applicable method is task assignment that dynamically adjusts crowd
workflow parameters. In this survey, we review task assignment methods that
address: heterogeneous task assignment, question assignment, and plurality
problems in crowdsourcing. We discuss and contrast how these methods estimate
worker performance, and highlight potential challenges in their implementation.
Finally, we discuss future research directions for task assignment methods, and
how crowdsourcing platforms and other stakeholders can benefit from them.
