  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, cyberbulling, 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 English
data available by applying cross-lingual contextual word embeddings and
transfer learning to make predictions in languages with less resources. We
project predictions on comparable data in Bengali, Hindi, and Spanish and we
report results of 0.8415 F1 macro for Bengali, 0.8568 F1 macro for Hindi, and
0.7513 F1 macro for Spanish. Finally, we show that our approach compares
favorably to the best systems submitted to recent shared tasks on these three
languages, confirming the robustness of cross-lingual contextual embeddings and
transfer learning for this task.
