# Offense Detection in Dravidian Languages using Code-Mixing Index based Focal Loss

arXiv: https://arxiv.org/abs/2111.06916 · HTML: https://mlsanity.com/paper/2111.06916/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 61 references that are in the map.
- First posted on arXiv 12 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [Large-Scale Long-Tailed Recognition in an Open World](https://mlsanity.com/paper/1904.05160/) (2019)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets](https://mlsanity.com/paper/2008.04277/) (2020)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [Polyglot: Distributed Word Representations for Multilingual NLP](https://mlsanity.com/paper/1307.1662/) (2013)
- [Hater-O-Genius Aggression Classification using Capsule Networks](https://mlsanity.com/paper/2105.11219/) (2021)
- [False-Friend Detection and Entity Matching via Unsupervised Transliteration](https://mlsanity.com/paper/1611.06722/) (2016)
- [Enhanced Offensive Language Detection Through Data Augmentation](https://mlsanity.com/paper/2012.02954/) (2020)
- [CMSAOne@Dravidian-CodeMix-FIRE2020: A Meta Embedding and Transformer model for Code-Mixed Sentiment Analysis on Social Media Text](https://mlsanity.com/paper/2101.09004/) (2021)
- [JUNLP@Dravidian-CodeMix-FIRE2020: Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags](https://mlsanity.com/paper/2010.10111/) (2020)
- [Task Adaptive Pretraining of Transformers for Hostility Detection](https://mlsanity.com/paper/2101.03382/) (2021)

## Built on by (most cited first, 6 of 6)

- [Memotion 3: Dataset on Sentiment and Emotion Analysis of Codemixed Hindi-English Memes](https://mlsanity.com/paper/2303.09892/) (2023)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling](https://mlsanity.com/paper/2309.05270/) (2023)
- [Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes](https://mlsanity.com/paper/2309.06517/) (2023)
- [LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs](https://mlsanity.com/paper/2412.17131/) (2024)
- [DeHate: A Stable Diffusion-based Multimodal Approach to Mitigate Hate Speech in Images](https://mlsanity.com/paper/2509.21787/) (2025)

## Abstract (arXiv)

Over the past decade, we have seen exponential growth in online content fueled by social media platforms. Data generation of this scale comes with the caveat of insurmountable offensive content in it. The complexity of identifying offensive content is exacerbated by the usage of multiple modalities (image, language, etc.), code-mixed language and more. Moreover, even after careful sampling and annotation of offensive content, there will always exist a significant class imbalance between offensive and non-offensive content. In this paper, we introduce a novel Code-Mixing Index (CMI) based focal loss which circumvents two challenges (1) code-mixing in languages (2) class imbalance problem for Dravidian language offense detection. We also replace the conventional dot product-based classifier with the cosine-based classifier which results in a boost in performance. Further, we use multilingual models that help transfer characteristics learnt across languages to work effectively with low resourced languages. It is also important to note that our model handles instances of mixed script (say usage of Latin and Dravidian-Tamil script) as well. To summarize, our model can handle offensive language detection in a low-resource, class imbalanced, multilingual and code-mixed setting.