# Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection

arXiv: https://arxiv.org/abs/2303.02513 · HTML: https://mlsanity.com/paper/2303.02513/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 72 references that are in the map.
- First posted on arXiv 4 Mar 2023.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [Learning to Generalize: Meta-Learning for Domain Generalization](https://mlsanity.com/paper/1710.03463/) (2017)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Meta-Learning Probabilistic Inference For Prediction](https://mlsanity.com/paper/1805.09921/) (2018)
- [Meta-Learning for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1808.08437/) (2018)
- [SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation](https://mlsanity.com/paper/1808.07512/) (2018)
- [Multilingual Offensive Language Identification with Cross-lingual Embeddings](https://mlsanity.com/paper/2010.05324/) (2020)
- [Sequence-Level Mixed Sample Data Augmentation](https://mlsanity.com/paper/2011.09039/) (2020)
- [Zero-Shot Cross-Lingual Transfer with Meta Learning](https://mlsanity.com/paper/2003.02739/) (2020)
- [Diverse Few-Shot Text Classification with Multiple Metrics](https://mlsanity.com/paper/1805.07513/) (2018)
- [Few-shot Text Classification with Distributional Signatures](https://mlsanity.com/paper/1908.06039/) (2019)
- [One-step and Two-step Classification for Abusive Language Detection on Twitter](https://mlsanity.com/paper/1706.01206/) (2017)
- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
- [Keep Calm and Switch On! Preserving Sentiment and Fluency in Semantic Text Exchange](https://mlsanity.com/paper/1909.00088/) (2019)
- [AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection](https://mlsanity.com/paper/2103.11800/) (2021)
- [Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification](https://mlsanity.com/paper/2107.12262/) (2021)
- [Fight Fire with Fire: Fine-tuning Hate Detectors using Large Samples of Generated Hate Speech](https://mlsanity.com/paper/2109.00591/) (2021)
- [Predicting Hate Intensity of Twitter Conversation Threads](https://mlsanity.com/paper/2206.08406/) (2022)
- [HateMonitors: Language Agnostic Abuse Detection in Social Media](https://mlsanity.com/paper/1909.12642/) (2019)
- [Cross-lingual Capsule Network for Hate Speech Detection in Social Media](https://mlsanity.com/paper/2108.03089/) (2021)

## Built on by (most cited first, 11 of 11)

- [Evaluating GPT-3 Generated Explanations for Hateful Content Moderation](https://mlsanity.com/paper/2305.17680/) (2023)
- [Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in Hateful Video Detection](https://mlsanity.com/paper/2501.15438/) (2025)
- [HateTinyLLM : Hate Speech Detection Using Tiny Large Language Models](https://mlsanity.com/paper/2405.01577/) (2024)
- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection](https://mlsanity.com/paper/2410.15623/) (2024)
- [Parameter-Efficient Fine-Tuning for Low-Resource Languages: A Comparative Study of LLMs for Bengali Hate Speech Detection](https://mlsanity.com/paper/2510.16985/) (2025)
- [A Survey on Automatic Online Hate Speech Detection in Low-Resource Languages](https://mlsanity.com/paper/2411.19017/) (2024)
- [Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model](https://mlsanity.com/paper/2404.09045/) (2024)
- [Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning](https://mlsanity.com/paper/2412.01408/) (2024)
- [CRAFT: Cultural Russian-Oriented Dataset Adaptation for Focused Text-to-Image Generation](https://mlsanity.com/paper/2505.04851/) (2025)
- [Comparison of Modern Multilingual Text Embedding Techniques for Hate Speech Detection Task](https://mlsanity.com/paper/2604.14907/) (2026)

## Abstract (arXiv)

Hate speech in social media is a growing phenomenon, and detecting such toxic content has recently gained significant traction in the research community. Existing studies have explored fine-tuning language models (LMs) to perform hate speech detection, and these solutions have yielded significant performance. However, most of these studies are limited to detecting hate speech only in English, neglecting the bulk of hateful content that is generated in other languages, particularly in low-resource languages. Developing a classifier that captures hate speech and nuances in a low-resource language with limited data is extremely challenging. To fill the research gap, we propose HateMAML, a model-agnostic meta-learning-based framework that effectively performs hate speech detection in low-resource languages. HateMAML utilizes a self-supervision strategy to overcome the limitation of data scarcity and produces better LM initialization for fast adaptation to an unseen target language (i.e., cross-lingual transfer) or other hate speech datasets (i.e., domain generalization). Extensive experiments are conducted on five datasets across eight different low-resource languages. The results show that HateMAML outperforms the state-of-the-art baselines by more than 3% in the cross-domain multilingual transfer setting. We also conduct ablation studies to analyze the characteristics of HateMAML.