Machine Translation into Low-resource Language Varieties

State-of-the-art machine translation (MT) systems are typically trained to generate "standard"target language; however, many languages have multiple varieties (regional varieties, dialects, sociolects, non-native varieties) that are different from the standard language. Such varieties are often low-resource, and hence do not benefit from contemporary NLP solutions, MT included. We propose a general framework to rapidly adapt MT systems to generate language varieties that are close to, but different from, the standard target language, using no parallel (source- variety) data. This also includes adaptation of MT systems to low-resource typologicallyrelated target languages.1 We experiment with adapting an English-Russian MT system to generate Ukrainian and Belarusian, an English-Norwegian Bokmål system to generate Nynorsk, and an English-Arabic system to generate four Arabic dialects, obtaining significant improvements over competitive baselines.