@inproceedings{kaczynski-przybyla-2021-homados,
    title = "{HOMADOS} at {S}em{E}val-2021 Task 6: Multi-Task Learning for Propaganda Detection",
    author = "Kaczy{\'n}ski, Konrad  and
      Przyby{\l}a, Piotr",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semeval-1.141/",
    doi = "10.18653/v1/2021.semeval-1.141",
    pages = "1027--1031",
    abstract = "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."
}