# NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers

arXiv: https://arxiv.org/abs/2103.00455 · HTML: https://mlsanity.com/paper/2103.00455/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Corpus Creation for Sentiment Analysis in Code-Mixed Tamil-English Text](https://mlsanity.com/paper/2006.00206/) (2020)
- [A Sentiment Analysis Dataset for Code-Mixed Malayalam-English](https://mlsanity.com/paper/2006.00210/) (2020)
- [ktrain: A Low-Code Library for Augmented Machine Learning](https://mlsanity.com/paper/2004.10703/) (2020)
- [Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media](https://mlsanity.com/paper/2101.03291/) (2021)
- [TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network](https://mlsanity.com/paper/2012.11420/) (2020)

## Built on by (most cited first, 9 of 9)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art](https://mlsanity.com/paper/2308.15745/) (2023)
- [Offensive Language Identification in Low-resourced Code-mixed Dravidian languages using Pseudo-labeling](https://mlsanity.com/paper/2108.12177/) (2021)
- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [LLM-Based Multi-Task Bangla Hate Speech Detection: Type, Severity, and Target](https://mlsanity.com/paper/2510.01995/) (2025)
- [Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and Posts](https://mlsanity.com/paper/2108.10939/) (2021)
- [Developing Successful Shared Tasks on Offensive Language Identification for Dravidian Languages](https://mlsanity.com/paper/2111.03375/) (2021)
- [MixSarc: A Bangla-English Code-Mixed Corpus for Implicit Meaning Identification](https://mlsanity.com/paper/2602.21608/) (2026)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)

## Abstract (arXiv)

The increasing accessibility of the internet facilitated social media usage and encouraged individuals to express their opinions liberally. Nevertheless, it also creates a place for content polluters to disseminate offensive posts or contents. Most of such offensive posts are written in a cross-lingual manner and can easily evade the online surveillance systems. This paper presents an automated system that can identify offensive text from multilingual code-mixed data. In the task, datasets provided in three languages including Tamil, Malayalam and Kannada code-mixed with English where participants are asked to implement separate models for each language. To accomplish the tasks, we employed two machine learning techniques (LR, SVM), three deep learning (LSTM, LSTM+Attention) techniques and three transformers (m-BERT, Indic-BERT, XLM-R) based methods. Results show that XLM-R outperforms other techniques in Tamil and Malayalam languages while m-BERT achieves the highest score in the Kannada language. The proposed models gained weighted f1 score of 0.76 (for Tamil), 0.93 (for Malayalam), and 0.71 (for Kannada) with a rank of 3rd, 5th and 4th respectively.