@inproceedings{pamies-etal-2020-lt,
    title = "{LT}@{H}elsinki at {S}em{E}val-2020 Task 12: Multilingual or Language-specific {BERT}?",
    author = {P{\`a}mies, Marc  and
      {\"O}hman, Emily  and
      Kajava, Kaisla  and
      Tiedemann, J{\"o}rg},
    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/jlcl-multiple-ingestion/2020.semeval-1.205/",
    doi = "10.18653/v1/2020.semeval-1.205",
    pages = "1569--1575",
    abstract = "This paper presents the different models submitted by the LT@Helsinki team for the SemEval 2020 Shared Task 12. Our team participated in sub-tasks A and C; titled offensive language identification and offense target identification, respectively. In both cases we used the so-called Bidirectional Encoder Representation from Transformer (BERT), a model pre-trained by Google and fine-tuned by us on the OLID and SOLID datasets. The results show that offensive tweet classification is one of several language-based tasks where BERT can achieve state-of-the-art results."
}