%0 Conference Proceedings %T HOMADOS at SemEval-2021 Task 6: Multi-Task Learning for Propaganda Detection %A Kaczyński, Konrad %A Przybyła, Piotr %Y Palmer, Alexis %Y Schneider, Nathan %Y Schluter, Natalie %Y Emerson, Guy %Y Herbelot, Aurelie %Y Zhu, Xiaodan %S Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F kaczynski-przybyla-2021-homados %X Among 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. %R 10.18653/v1/2021.semeval-1.141 %U https://aclanthology.org/2021.semeval-1.141 %U https://doi.org/10.18653/v1/2021.semeval-1.141 %P 1027-1031