mlsanityData to 14 Oct 2026

Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection

arXiv 2206.00962 ↗First posted 2 Jun 2022cs.CL cs.AI cs.LGcited by 8

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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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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 5; 2024: 1; 2025: 1; 2026: 1.

8 papers.