  We present a method for conducting morphological disambiguation for South
S\'ami, which is an endangered language. Our method uses an FST-based
morphological analyzer to produce an ambiguous set of morphological readings
for each word in a sentence. These readings are disambiguated with a Bi-RNN
model trained on the related North S\'ami UD Treebank and some synthetically
generated South S\'ami data. The disambiguation is done on the level of
morphological tags ignoring word forms and lemmas; this makes it possible to
use North S\'ami training data for South S\'ami without the need for a
bilingual dictionary or aligned word embeddings. Our approach requires only
minimal resources for South S\'ami, which makes it usable and applicable in the
contexts of any other endangered language as well.
