@inproceedings{perez-etal-2020-andes,
    title = "{ANDES} at {S}em{E}val-2020 Task 12: A Jointly-trained {BERT} Multilingual Model for Offensive Language Detection",
    author = "P{\'e}rez, Juan Manuel  and
      Arango, Aym{\'e}  and
      Luque, Franco",
    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/landing_page/2020.semeval-1.199/",
    doi = "10.18653/v1/2020.semeval-1.199",
    pages = "1524--1531",
    abstract = "This paper describes our participation in SemEval-2020 Task 12: Multilingual Offensive Language Detection. We jointly-trained a single model by fine-tuning Multilingual BERT to tackle the task across all the proposed languages: English, Danish, Turkish, Greek and Arabic. Our single model had competitive results, with a performance close to top-performing systems in spite of sharing the same parameters across all languages. Zero-shot and few-shot experiments were also conducted to analyze the transference performance among these languages. We make our code public for further research"
}