@inproceedings{dimov-etal-2020-nopropaganda,
    title = "{N}o{P}ropaganda at {S}em{E}val-2020 Task 11: A Borrowed Approach to Sequence Tagging and Text Classification",
    author = "Dimov, Ilya  and
      Korzun, Vladislav  and
      Smurov, Ivan",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.semeval-1.194/",
    doi = "10.18653/v1/2020.semeval-1.194",
    pages = "1488--1494",
    abstract = "This paper describes our contribution to SemEval-2020 Task 11: Detection Of Propaganda Techniques In News Articles. We start with simple LSTM baselines and move to an autoregressive transformer decoder to predict long continuous propaganda spans for the first subtask. We also adopt an approach from relation extraction by enveloping spans mentioned above with special tokens for the second subtask of propaganda technique classification. Our models report an F-score of 44.6{\%} and a micro-averaged F-score of 58.2{\%} for those tasks accordingly."
}