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.
%0 Conference Paper
%1 ls_leimeister
%A Herde, Marek
%A Huseljic, Denis
%A Sick, Bernhad
%A Bretschneider, Ulrich
%A Oeste-Reiss, Sarah
%B International Workshop & Tutorial on Interactive Adaptive Learning (IAL)
%C Torino, Italy
%D 2023
%K Artificial_Intelligence Crowdsourcing Crowdwork Crowdworker_Selection Deep_Learning cepub itegpub pub_soe pub_ubr
%T Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning
%U https://pubs.wi-kassel.de/wp-content/uploads/2023/10/JML_950.pdf
%X 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.
@inproceedings{ls_leimeister,
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.},
added-at = {2023-10-23T09:44:05.000+0200},
address = {Torino, Italy},
author = {Herde, Marek and Huseljic, Denis and Sick, Bernhad and Bretschneider, Ulrich and Oeste-Reiss, Sarah},
biburl = {https://www.bibsonomy.org/bibtex/243eaf5c0024491c6511de4566d30de0e/ls_leimeister},
booktitle = {International Workshop & Tutorial on Interactive Adaptive Learning (IAL)},
eventdate = {22 Sep 2023},
eventtitle = {International Workshop & Tutorial on Interactive Adaptive Learning (IAL)},
interhash = {f3cd1124be9a5f546e5610b41ac8a6ba},
intrahash = {43eaf5c0024491c6511de4566d30de0e},
issn = {1613-0073},
keywords = {Artificial_Intelligence Crowdsourcing Crowdwork Crowdworker_Selection Deep_Learning cepub itegpub pub_soe pub_ubr},
language = {English},
timestamp = {2023-10-23T10:40:59.000+0200},
title = {Who knows best? A Case Study on Intelligent Crowdworker Selection via Deep Learning},
url = {https://pubs.wi-kassel.de/wp-content/uploads/2023/10/JML_950.pdf},
venue = {Torino, Italy},
year = 2023
}