  Offensive content is pervasive in social media and a reason for concern to
companies and government organizations. Several studies have been recently
published investigating methods to detect the various forms of such content
(e.g. hate speech, cyberbullying, and cyberaggression). The clear majority of
these studies deal with English partially because most annotated datasets
available contain English data. In this paper, we take advantage of available
English datasets by applying cross-lingual contextual word embeddings and
transfer learning to make predictions in low-resource languages. We project
predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi,
Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in
TRAC-2 shared task, 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in
OffensEval 2020, 0.8568 F1 macro for Hindi in HASOC 2019 shared task and 0.7513
F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) showing that our
approach compares favourably to the best systems submitted to recent shared
tasks on these three languages. Additionally, we report competitive performance
on Arabic, and Turkish using the training and development sets of OffensEval
2020 shared task. The results for all languages confirm the robustness of
cross-lingual contextual embeddings and transfer learning for this task.
