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

arXiv: https://arxiv.org/abs/1809.08652 · HTML: https://mlsanity.com/paper/1809.08652/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Sep 2018.

## Built on by (most cited first, 11 of 11)

- [MTTM: Metamorphic Testing for Textual Content Moderation Software](https://mlsanity.com/paper/2302.05706/) (2023)
- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [Hate Me Not: Detecting Hate Inducing Memes in Code Switched Languages](https://mlsanity.com/paper/2204.11356/) (2022)
- [Hate Speech and Offensive Content Detection in Indo-Aryan Languages: A Battle of LSTM and Transformers](https://mlsanity.com/paper/2312.05671/) (2023)
- [Leveraging Transformers for Hate Speech Detection in Conversational Code-Mixed Tweets](https://mlsanity.com/paper/2112.09986/) (2021)
- [An Image is Worth a Thousand Toxic Words: A Metamorphic Testing Framework for Content Moderation Software](https://mlsanity.com/paper/2308.09810/) (2023)
- [Cisco at SemEval-2021 Task 5: What's Toxic?: Leveraging Transformers for Multiple Toxic Span Extraction from Online Comments](https://mlsanity.com/paper/2105.13959/) (2021)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Culture Matters in Toxic Language Detection in Persian](https://mlsanity.com/paper/2506.03458/) (2025)
- [Multilingual and Multimodal Abuse Detection](https://mlsanity.com/paper/2204.02263/) (2022)
- [YZR-net : Self-supervised Hidden representations Invariant to Transformations for profanity detection](https://mlsanity.com/paper/2211.15532/) (2022)

## Abstract (arXiv)

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