  Multilingual transfer techniques often improve low-resource machine
translation (MT). Many of these techniques are applied without considering data
characteristics. We show in the context of Haitian-to-English translation that
transfer effectiveness is correlated with amount of training data and
relationships between knowledge-sharing languages. Our experiments suggest that
for some languages beyond a threshold of authentic data, back-translation
augmentation methods are counterproductive, while cross-lingual transfer from a
sufficiently related language is preferred. We complement this finding by
contributing a rule-based French-Haitian orthographic and syntactic engine and
a novel method for phonological embedding. When used with multilingual
techniques, orthographic transformation makes statistically significant
improvements over conventional methods. And in very low-resource Jamaican MT,
code-switching with a transfer language for orthographic resemblance yields a
6.63 BLEU point advantage.
