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HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection

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cris.lastimport.scopus2024-02-12T20:31:59Z
dc.abstract.enAmong the tasks motivated by the proliferation of misinformation, propaganda detection is particularly challenging due to the deficit of fine-grained manual annotations required to train machine learning models. Here we show how data from other related tasks, including credibility assessment, can be leveraged in multi-task learning (MTL) framework to accelerate the training process. To that end, we design a BERT-based model with multiple output layers, train it in several MTL scenarios and perform evaluation against the SemEval gold standard.
dc.affiliationUniwersytet Warszawski
dc.contributor.authorPrzybyła, Piotr Michał
dc.contributor.authorKaczyński, Konrad
dc.date.accessioned2024-01-28T20:41:52Z
dc.date.available2024-01-28T20:41:52Z
dc.date.issued2021
dc.description.financeNie dotyczy
dc.identifier.doi10.18653/V1/2021.SEMEVAL-1.141
dc.identifier.urihttps://repozytorium.uw.edu.pl//handle/item/153596
dc.identifier.weblinkhttps://aclanthology.org/2021.semeval-1.141/
dc.languageeng
dc.publisher.ministerialAssociation for Computational Linguistics
dc.relation.pages1027-1031
dc.rightsClosedAccess
dc.sciencecloudnosend
dc.titleHOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection
dc.typeMonographChapter
dspace.entity.typePublication