@inproceedings{fajcik-etal-2020-fit,
    title = "{BUT}-{FIT} at {S}em{E}val-2020 Task 5: Automatic Detection of Counterfactual Statements with Deep Pre-trained Language Representation Models",
    author = "Fajcik, Martin  and
      Jon, Josef  and
      Docekal, Martin  and
      Smrz, Pavel",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2020.semeval-1.53/",
    doi = "10.18653/v1/2020.semeval-1.53",
    pages = "437--444",
    abstract = "This paper describes BUT-FIT`s submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text (Subtask 2). We experimented with various state-of-the-art language representation models (LRMs). We found RoBERTa LRM to perform the best in both subtasks. We achieved the first place in both exact match and F1 for Subtask 2 and ranked second for Subtask 1."
}