Abstract

While state-of-the-art models for machine translation and natural language understanding perform well on high-resource members of (some of) the world’s language families, they degrade on closely-related languages, dialects, and variants of these languages, which are typically much lower-resourced. However, a considerable part of the divergence between related languages is largely systematic and well-understood. In this talk, we first set up a framework within which we can understand performance degradation on a low-resource language as a function of linguistic distance from a high-resource neighbor, using artificial dialects generated at controlled distances from a given language.

Next, we introduce Dial Up, a method to induce robustness to the dialect continua of training languages in pretrained machine translation models, including for unseen dialects. We discuss the success of our approach as depending on various factors, and briefly talk about next directions in this space.