  Canonical morphological segmentation consists of dividing words into their
standardized morphemes. Here, we are interested in approaches for the task when
training data is limited. We compare model performance in a simulated
low-resource setting for the high-resource languages German, English, and
Indonesian to experiments on new datasets for the truly low-resource languages
Popoluca and Tepehua. We explore two new models for the task, borrowing from
the closely related area of morphological generation: an LSTM pointer-generator
and a sequence-to-sequence model with hard monotonic attention trained with
imitation learning. We find that, in the low-resource setting, the novel
approaches outperform existing ones on all languages by up to 11.4% accuracy.
However, while accuracy in emulated low-resource scenarios is over 50% for all
languages, for the truly low-resource languages Popoluca and Tepehua, our best
model only obtains 37.4% and 28.4% accuracy, respectively. Thus, we conclude
that canonical segmentation is still a challenging task for low-resource
languages.
