Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study thResearch goal: How does the domain similarity between auxiliary tasks and target hate speech detection influence the generalization performance of multilingual models in zero-shot cross-lingual transfer, measured by precision and recall on low-resource languages in the XTREME-R benchmark?Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.2/10.