@inproceedings{costa-etal-2023-clac,
    title = "{CL}a{C} at {S}em{E}val-2023 Task 3: Language Potluck {R}o{BERT}a Detects Online Persuasion Techniques in a Multilingual Setup",
    author = "Costa, Nelson Filipe  and
      Hamilton, Bryce  and
      Kosseim, Leila",
    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-1.223/",
    doi = "10.18653/v1/2023.semeval-1.223",
    pages = "1613--1618",
    abstract = "This paper presents our approach to the SemEval-2023 Task 3 to detect online persuasion techniques in a multilingual setup. Our classification system is based on the RoBERTa-base model trained predominantly on English to label the persuasion techniques across 9 different languages. Our system was able to significantly surpass the baseline performance in 3 of the 9 languages: English, Georgian and Greek. However, our wrong assumption that a single classification system trained predominantly on English could generalize well to other languages, negatively impacted our scores on the other 6 languages. In this paper, we provide a description of the reasoning behind the development of our final model and what conclusions may be drawn from its performance for future work."
}