@inproceedings{pauli-etal-2023-teamampa,
    title = "{T}eam{A}mpa at {S}em{E}val-2023 Task 3: Exploring Multilabel and Multilingual {R}o{BERT}a Models for Persuasion and Framing Detection",
    author = "Pauli, Amalie  and
      Sarabia, Rafael  and
      Derczynski, Leon  and
      Assent, Ira",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2023.semeval-1.117/",
    doi = "10.18653/v1/2023.semeval-1.117",
    pages = "847--855",
    abstract = "This paper describes our submission to theSemEval 2023 Task 3 on two subtasks: detectingpersuasion techniques and framing. Bothsubtasks are multi-label classification problems. We present a set of experiments, exploring howto get robust performance across languages usingpre-trained RoBERTa models. We test differentoversampling strategies, a strategy ofadding textual features from predictions obtainedwith related models, and present bothinconclusive and negative results. We achievea robust ranking across languages and subtaskswith our best ranking being nr. 1 for Subtask 3on Spanish."
}