  Morphological segmentation for polysynthetic languages is challenging,
because a word may consist of many individual morphemes and training data can
be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define
the state of the art for morphological segmentation in high-resource settings
and for (mostly) European languages, we first show that they also obtain
competitive performance for Mexican polysynthetic languages in minimal-resource
settings. We then propose two novel multi-task training approaches -one with,
one without need for external unlabeled resources-, and two corresponding data
augmentation methods, improving over the neural baseline for all languages.
Finally, we explore cross-lingual transfer as a third way to fortify our neural
model and show that we can train one single multi-lingual model for related
languages while maintaining comparable or even improved performance, thus
reducing the amount of parameters by close to 75%. We provide our morphological
segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for
future research.
