  Transfer learning has led to large gains in performance for nearly all NLP
tasks while making downstream models easier and faster to train. This has also
been extended to low-resourced languages, with some success. We investigate the
properties of cross-lingual transfer learning between ten low-resourced
languages, from the perspective of a named entity recognition task. We
specifically investigate how much adaptive fine-tuning and the choice of
transfer language affect zero-shot transfer performance. We find that models
that perform well on a single language often do so at the expense of
generalising to others, while models with the best generalisation to other
languages suffer in individual language performance. Furthermore, the amount of
data overlap between the source and target datasets is a better predictor of
transfer performance than either the geographical or genetic distance between
the languages.
