@inproceedings{blaschke-etal-2020-cyberwalle,
    title = "{C}yber{W}all{E} at {S}em{E}val-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection",
    author = "Blaschke, Verena  and
      Korniyenko, Maxim  and
      Tureski, Sam",
    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/fix-sig-urls/2020.semeval-1.192/",
    doi = "10.18653/v1/2020.semeval-1.192",
    pages = "1469--1480",
    abstract = "This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86{\%}) and 8 out of 31 teams in the TC subtask (F1-score: 57.37{\%})."
}