@inproceedings{fang-etal-2019-neural,
    title = "Neural Multi-Task Learning for Stance Prediction",
    author = "Fang, Wei  and
      Nadeem, Moin  and
      Mohtarami, Mitra  and
      Glass, James",
    editor = "Thorne, James  and
      Vlachos, Andreas  and
      Cocarascu, Oana  and
      Christodoulopoulos, Christos  and
      Mittal, Arpit",
    booktitle = "Proceedings of the Second Workshop on Fact Extraction and VERification (FEVER)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/iwcs-25-ingestion/D19-6603/",
    doi = "10.18653/v1/D19-6603",
    pages = "13--19",
    abstract = "We present a multi-task learning model that leverages large amount of textual information from existing datasets to improve stance prediction. In particular, we utilize multiple NLP tasks under both unsupervised and supervised settings for the target stance prediction task. Our model obtains state-of-the-art performance on a public benchmark dataset, Fake News Challenge, outperforming current approaches by a wide margin."
}