  Morphologically-rich polysynthetic languages present a challenge for NLP
systems due to data sparsity, and a common strategy to handle this issue is to
apply subword segmentation. We investigate a wide variety of supervised and
unsupervised morphological segmentation methods for four polysynthetic
languages: Nahuatl, Raramuri, Shipibo-Konibo, and Wixarika. Then, we compare
the morphologically inspired segmentation methods against Byte-Pair Encodings
(BPEs) as inputs for machine translation (MT) when translating to and from
Spanish. We show that for all language pairs except for Nahuatl, an
unsupervised morphological segmentation algorithm outperforms BPEs consistently
and that, although supervised methods achieve better segmentation scores, they
under-perform in MT challenges. Finally, we contribute two new morphological
segmentation datasets for Raramuri and Shipibo-Konibo, and a parallel corpus
for Raramuri--Spanish.
