  Most work on part-of-speech (POS) tagging is focused on high resource
languages, or examines low-resource and active learning settings through
simulated studies. We evaluate POS tagging techniques on an actual endangered
language, Griko. We present a resource that contains 114 narratives in Griko,
along with sentence-level translations in Italian, and provides gold
annotations for the test set. Based on a previously collected small corpus, we
investigate several traditional methods, as well as methods that take advantage
of monolingual data or project cross-lingual POS tags. We show that the
combination of a semi-supervised method with cross-lingual transfer is more
appropriate for this extremely challenging setting, with the best tagger
achieving an accuracy of 72.9%. With an applied active learning scheme, which
we use to collect sentence-level annotations over the test set, we achieve
improvements of more than 21 percentage points.
