One of the biggest challenges for requesters and platform providers of crowdsourcing is quality control, which is to expect high-quality results from crowd workers who are neither necessarily very capable nor motivated. A common approach to tackle this problem is to introduce redundancy, that is, to request multiple workers to work on the same tasks. For simple multiple-choice tasks, several statistical methods to aggregate the multiple answers have been pro- posed. However, these methods cannot always be applied to more general tasks with unstructured response formats such as article writing, program coding, and logo designing, which occupy the majority on most crowdsourcing marketplaces. In this paper, we propose an unsupervised statistical quality estimation method for such general crowdsourcing tasks. Our method is based on the two-stage procedure
multiple workers are first requested to work on the same tasks in the creation stage, and then another set of workers re- view and grade each artifact in the review stage. We model the ability of each author and the bias of each reviewer, and propose a two-stage probabilistic generative model using the graded response model in the item response theory. Experiments using several general crowdsourcing tasks show that our method outperforms popular vote aggregation methods, which implies that our method can deliver high quality results with lower costs.