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Title: Identifying Dependent Annotators in Crowdsourcing
Crowdsourcing is the learning paradigm that aims to combine noisy labels provided by a crowd of human annotators. To facilitate this label fusion, most contemporary crowdsourcing methods assume conditional independence between different annotators. Nevertheless, in many cases this assumption may not hold. This work investigates the effects of groups of correlated annotators in multiclass crowdsourced classification. To deal with this setup, a novel approach is developed to identify groups of dependent annotators via second-order moments of annotator responses. This in turn, enables appropriate dependence aware aggregation of annotator responses. Preliminary tests on synthetic and real data showcase the potential of the proposed approach.  more » « less
Award ID(s):
2220292 2212318 2312547 2126052 2128593 2103256
NSF-PAR ID:
10424925
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Asilomar Conference on Signals Systems and Computers
Page Range / eLocation ID:
1276 to 1280
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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