# Multilingual Offensive Language Identification with Cross-lingual Embeddings

arXiv: https://arxiv.org/abs/2010.05324 · HTML: https://mlsanity.com/paper/2010.05324/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 30 of 71)

- [Detecting Harmful Content On Online Platforms: What Platforms Need Vs. Where Research Efforts Go](https://mlsanity.com/paper/2103.00153/) (2021)
- [MUDES: Multilingual Detection of Offensive Spans](https://mlsanity.com/paper/2102.09665/) (2021)
- [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)
- [Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://mlsanity.com/paper/2204.12543/) (2022)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [Listening to Affected Communities to Define Extreme Speech: Dataset and Experiments](https://mlsanity.com/paper/2203.11764/) (2022)
- [An Exploratory Analysis of Multilingual Word-Level Quality Estimation with Cross-Lingual Transformers](https://mlsanity.com/paper/2106.00143/) (2021)
- [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)
- [Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive](https://mlsanity.com/paper/2301.12534/) (2023)
- [HateBR: A Large Expert Annotated Corpus of Brazilian Instagram Comments for Offensive Language and Hate Speech Detection](https://mlsanity.com/paper/2103.14972/) (2021)
- [Multimodal Hate Speech Detection from Bengali Memes and Texts](https://mlsanity.com/paper/2204.10196/) (2022)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [Comparing Approaches to Dravidian Language Identification](https://mlsanity.com/paper/2103.05552/) (2021)
- [Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2303.02513/) (2023)
- [Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection Models](https://mlsanity.com/paper/2210.13029/) (2022)
- [BanTH: A Multi-label Hate Speech Detection Dataset for Transliterated Bangla](https://mlsanity.com/paper/2410.13281/) (2024)
- [NLP-CUET@DravidianLangTech-EACL2021: Offensive Language Detection from Multilingual Code-Mixed Text using Transformers](https://mlsanity.com/paper/2103.00455/) (2021)
- [Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection](https://mlsanity.com/paper/2206.00962/) (2022)
- [Transferring Knowledge Distillation for Multilingual Social Event Detection](https://mlsanity.com/paper/2108.03084/) (2021)
- [Hate Speech Detection with Generalizable Target-aware Fairness](https://mlsanity.com/paper/2406.00046/) (2024)
- [WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework for Detecting Toxic Spans](https://mlsanity.com/paper/2104.04630/) (2021)
- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [Exploring multi-task multi-lingual learning of transformer models for hate speech and offensive speech identification in social media](https://mlsanity.com/paper/2101.11155/) (2021)
- [Cross-lingual Capsule Network for Hate Speech Detection in Social Media](https://mlsanity.com/paper/2108.03089/) (2021)
- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [On the rise of fear speech in online social media](https://mlsanity.com/paper/2303.10311/) (2023)
- [indicnlp@kgp at DravidianLangTech-EACL2021: Offensive Language Identification in Dravidian Languages](https://mlsanity.com/paper/2102.07150/) (2021)
- [Transformers to Fight the COVID-19 Infodemic](https://mlsanity.com/paper/2104.12201/) (2021)
- [DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domain](https://mlsanity.com/paper/2205.06025/) (2022)

## 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, cyberbulling, 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 English data available by applying cross-lingual contextual word embeddings and transfer learning to make predictions in languages with less resources. We project predictions on comparable data in Bengali, Hindi, and Spanish and we report results of 0.8415 F1 macro for Bengali, 0.8568 F1 macro for Hindi, and 0.7513 F1 macro for Spanish. Finally, we show that our approach compares favorably to the best systems submitted to recent shared tasks on these three languages, confirming the robustness of cross-lingual contextual embeddings and transfer learning for this task.