@inproceedings{grigorev-ivanov-2020-inno,
    title = "Inno at {S}em{E}val-2020 Task 11: Leveraging Pure Transfomer for Multi-Class Propaganda Detection",
    author = "Grigorev, Dmitry  and
      Ivanov, Vladimir",
    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-1.193/",
    doi = "10.18653/v1/2020.semeval-1.193",
    pages = "1481--1487",
    abstract = "The paper presents the solution of team {\textquotedblright}Inno{\textquotedblright} to a SEMEVAL 2020 task 11 {\textquotedblright}Detection of propaganda techniques in news articles{\textquotedblright}. The goal of the second subtask is to classify textual segments that correspond to one of the 18 given propaganda techniques in news articles dataset. We tested a pure Transformer-based model with an optimized learning scheme on the ability to distinguish propaganda techniques between each other. Our model showed 0:6 and 0:58 overall F1 score on validation set and test set accordingly and non-zero F1 score on each class on both sets."
}