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.

本文探讨了如何利用跨语境上下文词向量和迁移学习在使用资源较少的本地化语言中进行社交媒体中的冒犯内容检测，结果表明这个方法在孟加拉语、印地语和西班牙语等多种语言中的检测效率较高。