Comparison of Modern Multilingual Text Embedding Techniques for Hate Speech Detection Task
Online hate speech and abusive language pose a growing challenge for content moderation, especially in multilingual settings and for low-resource languages such as Lithuanian. This paper investigates to what extent modern multilingual sentence embedding models can support accurate hate speech detection in Lithuanian, Russian, and English, and how their performance depends on downstream modeling choices and feature dimensionality. We introduce LtHate, a new Lithuanian hate speech corpus derived from news portals and social networks, and benchmark six modern multilingual encoders (potion, gemma, bge, snow, jina, e5) on LtHate, RuToxic, and EnSuperset using a unified Python pipeline. For eac...
| Authors | Evaldas Vaiciukynas, Paulius Danenas, Linas Ablonskis et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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