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Fine-tuning of pre-trained transformer networks such as BERT yieldstate-of-the-art results for text classification tasks. Typically, fine-tuningis performed on task-specific training datasets in a supervised manner. One canalso fine-tune in unsupervised manner beforehand by further pre-training themasked language modeling (MLM) task. Hereby, in-domain data for unsupervisedMLM resembling the actual classification target dataset allows for domainadaptation of the model. In this paper, we compare current pre-trainedtransformer networks with and without MLM fine-tuning on their performance foroffensive language detection. Our MLM fine-tuned RoBERTa-based classifierofficially ranks 1st in the SemEval 2020 Shared Task~12 for the Englishlanguage. Further experiments with the ALBERT model even surpass this result.

UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection