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

By Raghav Kapoor, Yaman Kumar Singla, Kshitij Rajput, Rajiv Ratn Shah, Ponnurangam Kumaraguru, Roger Zimmermann. 2018

37 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

The Risk of Racial Bias in Hate Speech Detection (2019, methodology): “To understand the racial and dialectic bias in toxic language detection, we focus our analyses on two corpora of tweets (Davidson et al., 2017; Founta et al., 2018) that are widely used in hate speech detection (Park et al., 2018; van Aken et al., 2018; Kapoor et al., 2018; Alorainy et al., 2018 1.”
Hindi-English Hate Speech Detection: Author Profiling, Debiasing, and Practical Perspectives (2020, methodology): “(Kapoor et al. 2018) used LSTM based transfer learning on the HEOT dataset.”