@inproceedings{lee-etal-2021-unifying,
    title = "On Unifying Misinformation Detection",
    author = "Lee, Nayeon  and
      Li, Belinda Z.  and
      Wang, Sinong  and
      Fung, Pascale  and
      Ma, Hao  and
      Yih, Wen-tau  and
      Khabsa, Madian",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.naacl-main.432/",
    doi = "10.18653/v1/2021.naacl-main.432",
    pages = "5479--5485",
    abstract = "In this paper, we introduce UnifiedM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news, and verifying rumors. By grouping these tasks together, UnifiedM2 learns a richer representation of misinformation, which leads to state-of-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UnifiedM2`s learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and the model`s generalizability to unseen events."
}