@inproceedings{conforti-etal-2021-adversarial,
    title = "Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus.",
    author = "Conforti, Costanza  and
      Berndt, Jakob  and
      Basaldella, Marco  and
      Pilehvar, Mohammad Taher  and
      Giannitsarou, Chryssi  and
      Toxvaerd, Flavio  and
      Collier, Nigel",
    editor = "Toivonen, Hannu  and
      Boggia, Michele",
    booktitle = "Proceedings of the EACL Hackashop on News Media Content Analysis and Automated Report Generation",
    month = apr,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.hackashop-1.1",
    pages = "1--7",
    abstract = "Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.",
}