Mind Your Language: Abuse and Offense Detection for Code-Switched Languages

In multilingual societies like the Indian subcontinent, use of code-switched languages is much popular and convenient for the users. In this paper, we study offense and abuse detection in the code-switched pair of Hindi and English (i. e, Hinglish), the pair that is the most spoken. The task is made difficult due to non-fixed grammar, vocabulary, semantics and spellings of Hinglish language. We apply transfer learning and make a LSTM based model for hate speech classification. This model surpasses the performance shown by the current best models to establish itself as the state-of-the-art in the unexplored domain of Hinglish offensive text classification. We also release our model and the embeddings trained for research purposes.

Authors

• Raghav Kapoor Carnegie Mellon University
• Yaman Kumar Adobe Media and Data Science Research (MDSR) IIIT-Delhi University at Buffalo
• Kshitij Rajput
• Rajiv Ratn Shah Indraprastha Institute of Information Technology, Delhi
• Ponnurangam Kumaraguru International Institute of Information Technology, Hyderabad
• Roger Zimmermann National University of Singapore

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