Crowdsourcing with Meta-Knowledge Transfer (Student Abstract)


  • Sunyue Xu Nanjing University of Science and Technology
  • Jing Zhang Nanjing University of Science and Technology



Crowdsourcing Learning, Transfer Learning, Statistical Inference


When crowdsourced workers perform annotation tasks in an unfamiliar domain, their accuracy will dramatically decline due to the lack of expertise. Transferring knowledge from relevant domains can form a better representation for instances, which benefits the estimation of workers' expertise in truth inference models. However, existing knowledge transfer processes for crowdsourcing require a considerable number of well-collected instances in source domains. This paper proposes a novel truth inference model for crowdsourcing, where (meta-)knowledge is transferred by meta-learning and used in the estimation of workers' expertise. Our preliminary experiments demonstrate that the meta-knowledge transfer significantly reduces instances in source domains and increases the accuracy of truth inference.




How to Cite

Xu, S., & Zhang, J. (2022). Crowdsourcing with Meta-Knowledge Transfer (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 36(11), 13095-13096.