# Multilingual Offensive Language Identification for Low-resource Languages

arXiv: https://arxiv.org/abs/2105.05996 · HTML: https://mlsanity.com/paper/2105.05996/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 61 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 30 of 33)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [HateCheck: Functional Tests for Hate Speech Detection Models](https://mlsanity.com/paper/2012.15606/) (2020)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [Cross-Lingual Ability of Multilingual BERT: An Empirical Study](https://mlsanity.com/paper/1912.07840/) (2019)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification](https://mlsanity.com/paper/2004.14454/) (2020)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [Developing a Multilingual Annotated Corpus of Misogyny and Aggression](https://mlsanity.com/paper/2003.07428/) (2020)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [Arabic Offensive Language on Twitter: Analysis and Experiments](https://mlsanity.com/paper/2004.02192/) (2020)
- [Challenges in Discriminating Profanity from Hate Speech](https://mlsanity.com/paper/1803.05495/) (2018)
- [A Dictionary-based Approach to Racism Detection in Dutch Social Media](https://mlsanity.com/paper/1608.08738/) (2016)
- [Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models](https://mlsanity.com/paper/2010.03542/) (2020)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [Beheshti-NER: Persian Named Entity Recognition Using BERT](https://mlsanity.com/paper/2003.08875/) (2020)
- [BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media](https://mlsanity.com/paper/2010.06278/) (2020)
- [QutNocturnal@HASOC'19: CNN for Hate Speech and Offensive Content Identification in Hindi Language](https://mlsanity.com/paper/2008.12448/) (2020)
- [InfoMiner at WNUT-2020 Task 2: Transformer-based Covid-19 Informative Tweet Extraction](https://mlsanity.com/paper/2010.05327/) (2020)
- [WLV-RIT at HASOC-Dravidian-CodeMix-FIRE2020: Offensive Language Identification in Code-switched YouTube Comments](https://mlsanity.com/paper/2011.00559/) (2020)
- [Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara](https://mlsanity.com/paper/2011.05284/) (2020)
- [NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer](https://mlsanity.com/paper/2008.01354/) (2020)

## Built on by (most cited first, 22 of 22)

- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Cross-Cultural Transfer Learning for Chinese Offensive Language Detection](https://mlsanity.com/paper/2303.17927/) (2023)
- [Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages](https://mlsanity.com/paper/2112.09301/) (2021)
- [Multilingual Content Moderation: A Case Study on Reddit](https://mlsanity.com/paper/2302.09618/) (2023)
- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments](https://mlsanity.com/paper/2108.00057/) (2021)
- [Hate Speech Detection in Limited Data Contexts using Synthetic Data Generation](https://mlsanity.com/paper/2310.02876/) (2023)
- [A Text-to-Text Model for Multilingual Offensive Language Identification](https://mlsanity.com/paper/2312.03379/) (2023)
- [Promoting Constructive Deliberation: Reframing for Receptiveness](https://mlsanity.com/paper/2405.15067/) (2024)
- [Quantifying the Suicidal Tendency on Social Media: A Survey](https://mlsanity.com/paper/2110.03663/) (2021)
- [Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media](https://mlsanity.com/paper/2201.00961/) (2022)
- [SOLD: Sinhala Offensive Language Dataset](https://mlsanity.com/paper/2212.00851/) (2022)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities](https://mlsanity.com/paper/2412.04942/) (2024)
- [Battling Hateful Content in Indic Languages HASOC '21](https://mlsanity.com/paper/2110.12780/) (2021)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)
- [Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs](https://mlsanity.com/paper/2510.02351/) (2025)
- [A Survey on Online User Aggression: Content Detection and Behavioral Analysis on Social Media](https://mlsanity.com/paper/2311.09367/) (2023)
- [Developing Linguistic Patterns to Mitigate Inherent Human Bias in Offensive Language Detection](https://mlsanity.com/paper/2312.01787/) (2023)
- [Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying](https://mlsanity.com/paper/2404.03686/) (2024)
- [Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning](https://mlsanity.com/paper/2412.01408/) (2024)
- [Large Language Models for Toxic Language Detection in Low-Resource Balkan Languages](https://mlsanity.com/paper/2506.09992/) (2025)

## Abstract (arXiv)

Offensive content is pervasive in social media and a reason for concern to companies and government organizations. Several studies have been recently published investigating methods to detect the various forms of such content (e.g. hate speech, cyberbullying, and cyberaggression). The clear majority of these studies deal with English partially because most annotated datasets available contain English data. In this paper, we take advantage of available English datasets by applying cross-lingual contextual word embeddings and transfer learning to make predictions in low-resource languages. We project predictions on comparable data in Arabic, Bengali, Danish, Greek, Hindi, Spanish, and Turkish. We report results of 0.8415 F1 macro for Bengali in TRAC-2 shared task, 0.8532 F1 macro for Danish and 0.8701 F1 macro for Greek in OffensEval 2020, 0.8568 F1 macro for Hindi in HASOC 2019 shared task and 0.7513 F1 macro for Spanish in in SemEval-2019 Task 5 (HatEval) showing that our approach compares favourably to the best systems submitted to recent shared tasks on these three languages. Additionally, we report competitive performance on Arabic, and Turkish using the training and development sets of OffensEval 2020 shared task. The results for all languages confirm the robustness of cross-lingual contextual embeddings and transfer learning for this task.