Conference proceedings article

Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning



Publication Details
Authors:
Herde, M.; Huseljic, D.; Sick, B.; Bretschneider, U.; Oeste-Reiß, S.
Editor:
Bunse, Mirko; Hammer, Barbara; Krempl, Georg; Lemaire, Vincent; Tharwat, Alaa; Saadallah, Amal
Publisher:
CEUR Workshop Proceedings (RWTH Aachen)
Place:
Torino, Italy

Publication year:
2023
Pages range :
14-18
Book title:
International Workshop & Tutorial on Interactive Adaptive Learning (IAL)
Languages:
English


Abstract

Crowdworking is a popular approach for annotating large amounts of data to train deep neural networks. However, parts of the annotations are often erroneous. In a case study, we demonstrate how an intelligent crowdworker selection via deep learning reduces the number of erroneous annotations and, thus, the annotation costs of obtaining reliable data for training deep neural networks.


Keywords
Artificial_Intelligence, Crowdsourcing, Crowdwork, Crowdworker Selection, Deep Learning, itegpub, pub soe, pub ubr

Last updated on 2023-27-12 at 12:37