@inproceedings{mittal-nakov-2022-iitd,
    title = "{IITD} at {WANLP} 2022 Shared Task: Multilingual Multi-Granularity Network for Propaganda Detection",
    author = "Mittal, Shubham  and
      Nakov, Preslav",
    editor = "Bouamor, Houda  and
      Al-Khalifa, Hend  and
      Darwish, Kareem  and
      Rambow, Owen  and
      Bougares, Fethi  and
      Abdelali, Ahmed  and
      Tomeh, Nadi  and
      Khalifa, Salam  and
      Zaghouani, Wajdi",
    booktitle = "Proceedings of the Seventh Arabic Natural Language Processing Workshop (WANLP)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Hybrid)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2022.wanlp-1.63/",
    doi = "10.18653/v1/2022.wanlp-1.63",
    pages = "529--533",
    abstract = "We present our system for the two subtasks of the shared task on propaganda detection in Arabic, part of WANLP{'}2022. Subtask 1 is a multi-label classification problem to find the propaganda techniques used in a given tweet. Our system for this task uses XLM-R to predict probabilities for the target tweet to use each of the techniques. In addition to finding the techniques, subtask 2 further asks to identify the textual span for each instance of each technique that is present in the tweet; the task can be modelled as a sequence tagging problem. We use a multi-granularity network with mBERT encoder for subtask 2. Overall, our system ranks second for both subtasks (out of 14 and 3 participants, respectively). Our experimental results and analysis show that it does not help to use a much larger English corpus annotated with propaganda techniques, regardless of whether used in English or after translation to Arabic."
}