# MUDES: Multilingual Detection of Offensive Spans

arXiv: https://arxiv.org/abs/2102.09665 · HTML: https://mlsanity.com/paper/2102.09665/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 52 references that are in the map.
- First posted on arXiv 18 Feb 2021.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [How to Fine-Tune BERT for Text Classification?](https://mlsanity.com/paper/1905.05583/) (2019)
- [Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification](https://mlsanity.com/paper/1903.04561/) (2019)
- [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)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Fine-Grained Analysis of Propaganda in News Articles](https://mlsanity.com/paper/1910.02517/) (2019)
- [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)
- [Offensive Language and Hate Speech Detection for Danish](https://mlsanity.com/paper/1908.04531/) (2019)
- [Detecting Hate Speech in Social Media](https://mlsanity.com/paper/1712.06427/) (2017)
- [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)
- [Getting deep recommenders fit: Bloom embeddings for sparse binary input/output networks](https://mlsanity.com/paper/1706.03993/) (2017)
- [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)
- [BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media](https://mlsanity.com/paper/2010.06278/) (2020)
- [UHH-LT at SemEval-2020 Task 12: Fine-Tuning of Pre-Trained Transformer Networks for Offensive Language Detection](https://mlsanity.com/paper/2004.11493/) (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)
- [NLPDove at SemEval-2020 Task 12: Improving Offensive Language Detection with Cross-lingual Transfer](https://mlsanity.com/paper/2008.01354/) (2020)
- [Detecting Online Hate Speech: Approaches Using Weak Supervision and Network Embedding Models](https://mlsanity.com/paper/2007.12724/) (2020)

## Built on by (most cited first, 30 of 30)

- [FBERT: A Neural Transformer for Identifying Offensive Content](https://mlsanity.com/paper/2109.05074/) (2021)
- [Cross-lingual Offensive Language Identification for Low Resource Languages: The Case of Marathi](https://mlsanity.com/paper/2109.03552/) (2021)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers](https://mlsanity.com/paper/2106.00143/) (2021)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [KOLD: Korean Offensive Language Dataset](https://mlsanity.com/paper/2205.11315/) (2022)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans](https://mlsanity.com/paper/2104.04630/) (2021)
- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [Fake News Detection: Comparative Evaluation of BERT-like Models and Large Language Models with Generative AI-Annotated Data](https://mlsanity.com/paper/2412.14276/) (2024)
- [Transformers to Fight the COVID-19 Infodemic](https://mlsanity.com/paper/2104.12201/) (2021)
- [Cross-Domain Toxic Spans Detection](https://mlsanity.com/paper/2306.09642/) (2023)
- [Muted: Multilingual Targeted Offensive Speech Identification and Visualization](https://mlsanity.com/paper/2312.11344/) (2023)
- [A Federated Learning Approach to Privacy Preserving Offensive Language Identification](https://mlsanity.com/paper/2404.11470/) (2024)
- [TransWiC at SemEval-2021 Task 2: Transformer-based Multilingual and Cross-lingual Word-in-Context Disambiguation](https://mlsanity.com/paper/2104.04632/) (2021)
- [WLV-RIT at GermEval 2021: Multitask Learning with Transformers to Detect Toxic, Engaging, and Fact-Claiming Comments](https://mlsanity.com/paper/2108.00057/) (2021)
- [Evaluating Open-Source Vision-Language Models for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2510.11852/) (2025)
- [Exploring Hate Speech Detection with HateXplain and BERT](https://mlsanity.com/paper/2208.04489/) (2022)
- [SOLD: Sinhala Offensive Language Dataset](https://mlsanity.com/paper/2212.00851/) (2022)
- [Overview of the HASOC Subtrack at FIRE 2023: Identification of Tokens Contributing to Explicit Hate in English by Span Detection](https://mlsanity.com/paper/2311.09834/) (2023)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [MasonPerplexity at Multimodal Hate Speech Event Detection 2024: Hate Speech and Target Detection Using Transformer Ensembles](https://mlsanity.com/paper/2402.01967/) (2024)
- [A Federated Approach to Few-Shot Hate Speech Detection for Marginalized Communities](https://mlsanity.com/paper/2412.04942/) (2024)
- [Towards Generalized Offensive Language Identification](https://mlsanity.com/paper/2407.18738/) (2024)
- [Language, Culture, and Ideology: Personalizing Offensiveness Detection in Political Tweets with Reasoning LLMs](https://mlsanity.com/paper/2510.02351/) (2025)
- [Transformer-based Detection of Multiword Expressions in Flower and Plant Names](https://mlsanity.com/paper/2209.08016/) (2022)
- [Deep Learning Methods for Extracting Metaphorical Names of Flowers and Plants](https://mlsanity.com/paper/2305.10833/) (2023)
- [WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data](https://mlsanity.com/paper/2509.01379/) (2025)
- [Exploring the Performance of Large Language Models on Subjective Span Identification Tasks](https://mlsanity.com/paper/2601.00736/) (2026)
- [PEACE 2.0: Grounded Explanations and Counter-Speech for Combating Hate Expressions](https://mlsanity.com/paper/2602.17467/) (2026)

## Abstract (arXiv)

The interest in offensive content identification in social media has grown substantially in recent years. Previous work has dealt mostly with post level annotations. However, identifying offensive spans is useful in many ways. To help coping with this important challenge, we present MUDES, a multilingual system to detect offensive spans in texts. MUDES features pre-trained models, a Python API for developers, and a user-friendly web-based interface. A detailed description of MUDES' components is presented in this paper.