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.

研究表明，对于低资源机器翻译，多语言转移技术的有效性与训练数据量以及知识共享语言之间的关系有关，当某些语言超过真实数据的阈值时，回译增强方法会适得其反，而跨语言转移则更合适。同时，该研究提出了一种基于规则的法语 - 海地克里奥尔语正字和句法引擎和一种新型的音韵嵌入方法，这些方法使得正字转换在多语言技术中相对于传统方法显著提高效果。在极低资源的牙买加机器翻译中，通过与一种具有正字相似度的转移语言进行代码交换，可以获得 6.63 BLEU 点的优势。