  Generalization is of particular importance in resource-constrained settings,
where the available training data may represent only a small fraction of the
distribution of possible texts. We investigate the ability of morpheme labeling
models to generalize by evaluating their performance on unseen genres of text,
and we experiment with strategies for closing the gap between performance on
in-distribution and out-of-distribution data. Specifically, we use weight decay
optimization, output denoising, and iterative pseudo-labeling, and achieve a 2%
improvement on a test set containing texts from unseen genres. All experiments
are performed using texts written in the Mayan language Uspanteko.
