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dc.contributor.authorHan, Guangyang
dc.contributor.authorYu, Guoxian
dc.contributor.authorLiu, Lei
dc.contributor.authorCui, Lizhen
dc.contributor.authorDomeniconi, Carlotta
dc.contributor.authorZhang, Xiangliang
dc.date.accessioned2021-11-11T08:27:45Z
dc.date.available2021-11-11T08:27:45Z
dc.date.issued2021-11-07
dc.identifier.urihttp://hdl.handle.net/10754/673316
dc.description.abstractWe raise and define a new crowdsourcing scenario, open set crowdsourcing, where we only know the general theme of an unfamiliar crowdsourcing project, and we don't know its label space, that is, the set of possible labels. This is still a task annotating problem, but the unfamiliarity with the tasks and the label space hampers the modelling of the task and of workers, and also the truth inference. We propose an intuitive solution, OSCrowd. First, OSCrowd integrates crowd theme related datasets into a large source domain to facilitate partial transfer learning to approximate the label space inference of these tasks. Next, it assigns weights to each source domain based on category correlation. After this, it uses multiple-source open set transfer learning to model crowd tasks and assign possible annotations. The label space and annotations given by transfer learning will be used to guide and standardize crowd workers' annotations. We validate OSCrowd in an online scenario, and prove that OSCrowd solves the open set crowdsourcing problem, works better than related crowdsourcing solutions.
dc.publisherarXiv
dc.relation.urlhttps://arxiv.org/pdf/2111.04073.pdf
dc.rightsArchived with thanks to arXiv
dc.titleOpen-Set Crowdsourcing using Multiple-Source Transfer Learning
dc.typePreprint
dc.eprint.versionPre-print
dc.contributor.institutionCollege of Computer and Information Sciences, Southwest University, China
dc.contributor.institutionSchool of Software, Shandong University, China
dc.contributor.institutionJoint SDU-NTU Centre for Artificial Intelligence Research, Shandong University, Jinan, China
dc.contributor.institutionDepartment of Computer Science, George Mason University, USA
dc.contributor.institutionDepartment of Computer Science, King Abudullah University of Science and Technology, Saudi Arabia
dc.identifier.arxivid2111.04073
refterms.dateFOA2021-11-11T08:28:49Z


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