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This paper describes our approach to the task of identifying offensivelanguages in a multilingual setting. We investigate two data augmentationstrategies: using additional semi-supervised labels with different thresholdsand cross-lingual transfer with data selection. Leveraging the semi-superviseddataset resulted in performance improvements compared to the baseline trainedsolely with the manually-annotated dataset. We propose a new metric,Translation Embedding Distance, to measure the transferability of instances forcross-lingual data selection. We also introduce various preprocessing stepstailored for social media text along with methods to fine-tune the pre-trainedmultilingual BERT (mBERT) for offensive language identification. Ourmultilingual systems achieved competitive results in Greek, Danish, and Turkishat OffensEval 2020.

NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer