Learning representations for quality estimation of crowdsourced submissions

•The problem of quality estimation of crowdsourced work is of great importance.•We aim to find high-quality work for more general tasks, such as article writing.•We exploit requesters historical feedback and directly model the submission quality.•Our embedding-based feature-learning methods outperform feature-engineering methods.•Our methods do not require additional crowdsourced grading.