@inproceedings{glazkova-glazkov-2022-detecting,
    title = "Detecting generated scientific papers using an ensemble of transformer models",
    author = "Glazkova, Anna  and
      Glazkov, Maksim",
    editor = "Cohan, Arman  and
      Feigenblat, Guy  and
      Freitag, Dayne  and
      Ghosal, Tirthankar  and
      Herrmannova, Drahomira  and
      Knoth, Petr  and
      Lo, Kyle  and
      Mayr, Philipp  and
      Shmueli-Scheuer, Michal  and
      de Waard, Anita  and
      Wang, Lucy Lu",
    booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.sdp-1.28/",
    pages = "223--228",
    abstract = "The paper describes neural models developed for the DAGPap22 shared task hosted at the Third Workshop on Scholarly Document Processing. This shared task targets the automatic detection of generated scientific papers. Our work focuses on comparing different transformer-based models as well as using additional datasets and techniques to deal with imbalanced classes. As a final submission, we utilized an ensemble of SciBERT, RoBERTa, and DeBERTa fine-tuned using random oversampling technique. Our model achieved 99.24{\%} in terms of F1-score. The official evaluation results have put our system at the third place."
}