@inproceedings{ek-ghanimifard-2019-synthetic,
    title = "Synthetic Propaganda Embeddings To Train A Linear Projection",
    author = "Ek, Adam  and
      Ghanimifard, Mehdi",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Barr{\'o}n-Cede{\~n}o, Alberto  and
      Brew, Chris  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D19-5023/",
    doi = "10.18653/v1/D19-5023",
    pages = "155--161",
    abstract = "This paper presents a method of detecting fine-grained categories of propaganda in text. Given a sentence, our method aims to identify a span of words and predict the type of propaganda used. To detect propaganda, we explore a method for extracting features of propaganda from contextualized embeddings without fine-tuning the large parameters of the base model. We show that by generating synthetic embeddings we can train a linear function with ReLU activation to extract useful labeled embeddings from an embedding space generated by a general-purpose language model. We also introduce an inference technique to detect continuous spans in sequences of propaganda tokens in sentences. A result of the ensemble model is submitted to the first shared task in fine-grained propaganda detection at NLP4IF as Team Stalin. In this paper, we provide additional analysis regarding our method of detecting spans of propaganda with synthetically generated representations."
}