Learning representations for quality estimation of crowdsourced submissions
作者：
Highlights：
• 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.
摘要
•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.
论文关键词：Crowdsourcing,Quality estimation,Embedding
论文评审过程：Received 22 June 2018, Revised 24 October 2018, Accepted 31 October 2018, Available online 28 December 2018, Version of Record 14 May 2019.
论文官网地址：https://doi.org/10.1016/j.ipm.2018.10.020
