@inproceedings{fajcik-etal-2019-fit,
    title = "{BUT}-{FIT} at {S}em{E}val-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers",
    author = "Fajcik, Martin  and
      Smrz, Pavel  and
      Burget, Lukas",
    editor = "May, Jonathan  and
      Shutova, Ekaterina  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Apidianaki, Marianna  and
      Mohammad, Saif M.",
    booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/S19-2192/",
    doi = "10.18653/v1/S19-2192",
    pages = "1097--1104",
    abstract = "This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019). The challenge focused on classifying whether posts from Twitter and Reddit support, deny, query, or comment a hidden rumour, truthfulness of which is the topic of an underlying discussion thread. We formulate the problem as a stance classification, determining the rumour stance of a post with respect to the previous thread post and the source thread post. The recent BERT architecture was employed to build an end-to-end system which has reached the F1 score of 61.67 {\%} on the provided test data. Without any hand-crafted feature, the system finished at the 2nd place in the competition, only 0.2 {\%} behind the winner."
}