  Parsers are available for only a handful of the world's languages, since they
require lots of training data. How far can we get with just a small amount of
training data? We systematically compare a set of simple strategies for
improving low-resource parsers: data augmentation, which has not been tested
before; cross-lingual training; and transliteration. Experimenting on three
typologically diverse low-resource languages---North S\'ami, Galician, and
Kazah---We find that (1) when only the low-resource treebank is available, data
augmentation is very helpful; (2) when a related high-resource treebank is
available, cross-lingual training is helpful and complements data augmentation;
and (3) when the high-resource treebank uses a different writing system,
transliteration into a shared orthographic spaces is also very helpful.
