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Multilingual language models have pushed state-of-the-art in cross-lingualNLP transfer. The majority of zero-shot cross-lingual transfer, however, useone and the same massively multilingual transformer (e.g., mBERT or XLM-R) totransfer to all target languages, irrespective of their typological,etymological, and phylogenetic relations to other languages. In particular,readily available data and models of resource-rich sibling languages are oftenignored. In this work, we empirically show, in a case study for Faroese -- alow-resource language from a high-resource language family -- that byleveraging the phylogenetic information and departing from the'one-size-fits-all' paradigm, one can improve cross-lingual transfer tolow-resource languages. In particular, we leverage abundant resources of otherScandinavian languages (i.e., Danish, Norwegian, Swedish, and Icelandic) forthe benefit of Faroese. Our evaluation results show that we can substantiallyimprove the transfer performance to Faroese by exploiting data and models ofclosely-related high-resource languages. Further, we release a new web corpusof Faroese and Faroese datasets for named entity recognition (NER), semantictext similarity (STS), and new language models trained on all Scandinavianlanguages.

Transfer to a Low-Resource Language via Close Relatives: The Case Study on Faroese