Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection
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Abstract (arXiv)
We study the selection of transfer languages for automatic abusive language detection. Instead of preparing a dataset for every language, we demonstrate the effectiveness of cross-lingual transfer learning for zero-shot abusive language detection. This way we can use existing data from higher-resource languages to build better detection systems for low-resource languages. Our datasets are from seven different languages from three language families. We measure the distance between the languages using several language similarity measures, especially by quantifying the World Atlas of Language Structures. We show that there is a correlation between linguistic similarity and classifier performance. This discovery allows us to choose an optimal transfer language for zero shot abusive language detection.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 28 of its 82 references that are in the map. First posted on arXiv 2 Jun 2022.
Builds on
28 of 82 references.
References · 28
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16 more
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Citing papers per year: 2023: 5; 2024: 1; 2025: 1; 2026: 1.
8 papers.
- 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Reviewcited by 50
- 2023Zero-shot cross-lingual transfer language selection using linguistic similaritycited by 28
- 2023Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Featurescited by 18
- 2025Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Taggingcited by 9
- 2023Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Artcited by 8
- 2024Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challengescited by 2
- 2023Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detectioncited by 1
- 2026Linguistic Distance Segregates Latent Representations in Automatic Speech Recognition Systemscited by 0
- 2026Linguistic Distance Segregates Latent Representations in Automatic Speech Recognition Systems31 Aug 2026
- 2025Untangling the Influence of Typology, Data and Model Architecture on Ranking Transfer Languages for Cross-Lingual POS Tagging25 Mar 2025
- 2024Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges17 Jan 2024
- 2023Vicinal Risk Minimization for Few-Shot Cross-lingual Transfer in Abusive Language Detection3 Nov 2023
- 2023Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features10 Oct 2023
- 2023Cyberbullying Detection for Low-resource Languages and Dialects: Review of the State of the Art30 Aug 2023
- 2023Towards a Common Understanding of Contributing Factors for Cross-Lingual Transfer in Multilingual Language Models: A Review26 May 2023
- 2023Zero-shot cross-lingual transfer language selection using linguistic similarity31 Jan 2023