Identification (biology)

Transfer of learning

Macro

Government (linguistics)

Word identification

Biology

Gene

Language identification

World Wide Web

Word recognition

Philosophy

Offensive

Artificial intelligence

Management

Bengali

Natural language

Economics

Chemistry

Programming language

Natural language processing

Computer science

Linguistics

Task (project management)

Social media

Botany

Hindi

Reading (process)

Biochemistry

Robustness (evolution)

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.

Multilingual Offensive Language Identification with Cross-lingual Embeddings