Most of the world’s languages and dialects are low-resource, and lack support in mainstream machine translation (MT) models. However, many of them have a closely-related high-resource language (HRL) neighbor, and differ in linguistically regular ways from it. This underscores the importance of model robustness to dialectal variation and cross-lingual generalization to the HRL dialect continuum. We present DialUp, consisting of a training-time technique for adapting a pretrained model to dialectal data (M–>D), and an inference-time intervention adapting dialectal data to the model expertise (D–>M). M–>D induces model robustness to potentially unseen and unknown dialects by exposure to synthetic data exemplifying linguistic mechanisms of dialectal variation, whereas D–>M treats dialectal divergence for known target dialects. These methods show considerable performance gains for several dialects from four language families, and modest gains for two other language families. We also conduct feature and error analyses, which show that language varieties with low baseline MT performance are more likely to benefit from these approaches.

When developing language technology, researchers have routinely turned to transfer learning to resolve the data scarcity conundrum presented in low-resource languages. As far as we know, this study is the first to evaluate the amount of documentation needed for transfer learning, specifically the smallest vocabulary size needed to create a sentence embedding space. In adopting widely spoken languages as a proxy for low-resource languages, our experiments show that the relationship between a sentence embedding’s vocabulary size and performance is logarithmic with performance leveling at a vocabulary size of 25,000. It should be noted that this relationship cannot be replicated across all languages and this level of documentation does not exist for many low-resource languages. We do observe, however, that performance accelerates at a vocabulary size of ≤ 1000, a quantity that is present in most low-resource language documentation. These results can aid researchers in understanding whether a low-resource language has enough documentation necessary to support the creation of a sentence embedding and language model.