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Offensive language detection is one of the most challenging problem in thenatural language processing field, being imposed by the rising presence of thisphenomenon in online social media. This paper describes our Transformer-basedsolutions for identifying offensive language on Twitter in five languages(i.e., English, Arabic, Danish, Greek, and Turkish), which was employed inSubtask A of the Offenseval 2020 shared task. Several neural architectures(i.e., BERT, mBERT, Roberta, XLM-Roberta, and ALBERT), pre-trained using bothsingle-language and multilingual corpora, were fine-tuned and compared usingmultiple combinations of datasets. Finally, the highest-scoring models wereused for our submissions in the competition, which ranked our team 21st of 85,28th of 53, 19th of 39, 16th of 37, and 10th of 46 for English, Arabic, Danish,Greek, and Turkish, respectively.

UPB at SemEval-2020 Task 12: Multilingual Offensive Language Detection on Social Media by Fine-tuning a Variety of BERT-based Models