@inproceedings{suhane-kowshik-2021-multi,
    title = "Multi Output Learning using Task Wise Attention for Predicting Binary Properties of Tweets : Shared-Task-On-Fighting the {COVID}-19 Infodemic",
    author = "Suhane, Ayush  and
      Kowshik, Shreyas",
    editor = "Feldman, Anna  and
      Da San Martino, Giovanni  and
      Leberknight, Chris  and
      Nakov, Preslav",
    booktitle = "Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2021.nlp4if-1.16/",
    doi = "10.18653/v1/2021.nlp4if-1.16",
    pages = "110--114",
    abstract = "In this paper, we describe our system for the shared task on Fighting the COVID-19 Infodemic in the English Language. Our proposed architecture consists of a multi-output classification model for the seven tasks, with a task-wise multi-head attention layer for inter-task information aggregation. This was built on top of the Bidirectional Encoder Representations obtained from the RoBERTa Transformer. We were able to achieve a mean F1 score of 0.891 on the test data, leading us to the second position on the test-set leaderboard."
}