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

Authors

Raghav Kapoor

Netaji Subhas Institute of Technology


Yaman Kumar

Indian Institute of Technology Delhi


Kshitij Rajput

Netaji Subhas Institute of Technology


Rajiv Ratn Shah

Indian Institute of Technology Delhi


Ponnurangam Kumaraguru

Indian Institute of Technology Delhi


Roger Zimmermann

National University of Singaport


Proceedings:

No. 1: AAAI-19, IAAI-19, EAAI-20

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 33

Track:

Student Abstract Track

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Abstract:

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.

DOI:

10.1609/aaai.v33i01.33019951


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 33