@inproceedings{singh-etal-2020-newssweeper,
    title = "news{S}weeper at {S}em{E}val-2020 Task 11: Context-Aware Rich Feature Representations for Propaganda Classification",
    author = "Singh, Paramansh  and
      Sandhu, Siraj  and
      Kumar, Subham  and
      Modi, Ashutosh",
    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-1.231/",
    doi = "10.18653/v1/2020.semeval-1.231",
    pages = "1764--1770",
    abstract = "This paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We make use of pre-trained BERT language model enhanced with tagging techniques developed for the task of Named Entity Recognition (NER), to develop a system for identifying propaganda spans in the text. For the second subtask, we incorporate contextual features in a pre-trained RoBERTa model for the classification of propaganda techniques. We were ranked 5th in the propaganda technique classification subtask."
}