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
@article{arxiv.1809.08652,
title = {Mind Your Language: Abuse and Offense Detection for Code-Switched Languages},
author = {Raghav Kapoor and Yaman Kumar and Kshitij Rajput and Rajiv Ratn Shah and Ponnurangam Kumaraguru and Roger Zimmermann},
journal= {arXiv preprint arXiv:1809.08652},
year = {2018}
}