mlsanityData to 7 Oct 2026

Cross-lingual Hate Speech Detection using Transformer Models

arXiv 2111.00981 ↗First posted 1 Nov 2021cs.CLcited by 3

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Abstract (arXiv)

Hate speech detection within a cross-lingual setting represents a paramount area of interest for all medium and large-scale online platforms. Failing to properly address this issue on a global scale has already led over time to morally questionable real-life events, human deaths, and the perpetuation of hate itself. This paper illustrates the capabilities of fine-tuned altered multi-lingual Transformer models (mBERT, XLM-RoBERTa) regarding this crucial social data science task with cross-lingual training from English to French, vice-versa and each language on its own, including sections about iterative improvement and comparative error analysis.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 1 Nov 2021.

Builds on

8 of 30 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2019Racial Bias in Hate Speech and Abusive Language Detection Datasetscited by 208
  3. 2017Deep Learning for Hate Speech Detection in Tweetscited by 180
  4. 2019Multilingual and Multi-Aspect Hate Speech Analysiscited by 133
  5. 2019CONAN -- COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speechcited by 109
  6. 2017Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisiscited by 95
  7. 2019A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Mediacited by 95
  8. 2018Hate Speech Detection: A Solved Problem? The Challenging Case of Long Tail on Twittercited by 36

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.