# OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification

arXiv: https://arxiv.org/abs/2310.18387 · HTML: https://mlsanity.com/paper/2310.18387/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 47 references that are in the map.
- First posted on arXiv 27 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [MuRIL: Multilingual Representations for Indian Languages](https://mlsanity.com/paper/2103.10730/) (2021)
- [BanglaBERT: Language Model Pretraining and Benchmarks for Low-Resource Language Understanding Evaluation in Bangla](https://mlsanity.com/paper/2101.00204/) (2021)
- [SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification](https://mlsanity.com/paper/2004.14454/) (2020)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [L3Cube-HingCorpus and HingBERT: A Code Mixed Hindi-English Dataset and BERT Language Models](https://mlsanity.com/paper/2204.08398/) (2022)
- [FBERT: A Neural Transformer for Identifying Offensive Content](https://mlsanity.com/paper/2109.05074/) (2021)
- [Harnessing Code Switching to Transcend the Linguistic Barrier](https://mlsanity.com/paper/2001.11258/) (2020)
- [Cross-Lingual Text Classification of Transliterated Hindi and Malayalam](https://mlsanity.com/paper/2108.13620/) (2021)

## Built on by (most cited first, 5 of 5)

- [Beyond Monolingual Assumptions: A Survey of Code-Switched NLP in the Era of Large Language Models across Modalities](https://mlsanity.com/paper/2510.07037/) (2025)
- [EmoMix-3L: A Code-Mixed Dataset for Bangla-English-Hindi Emotion Detection](https://mlsanity.com/paper/2405.06922/) (2024)
- [MasonTigers at SemEval-2024 Task 10: Emotion Discovery and Flip Reasoning in Conversation with Ensemble of Transformers and Prompting](https://mlsanity.com/paper/2407.00581/) (2024)
- [NLP Privacy Risk Identification in Social Media (NLP-PRISM): A Survey](https://mlsanity.com/paper/2602.15866/) (2026)
- [IndicTriMix: Developing Language Identification Datasets and Models for Tri-Language Code-Mixing](https://mlsanity.com/paper/2609.11851/) (2026)

## Abstract (arXiv)

Code-mixing is a well-studied linguistic phenomenon when two or more languages are mixed in text or speech. Several works have been conducted on building datasets and performing downstream NLP tasks on code-mixed data. Although it is not uncommon to observe code-mixing of three or more languages, most available datasets in this domain contain code-mixed data from only two languages. In this paper, we introduce OffMix-3L, a novel offensive language identification dataset containing code-mixed data from three different languages. We experiment with several models on this dataset and observe that BanglishBERT outperforms other transformer-based models and GPT-3.5.