Train Global, Tailor Local: Minimalist Multilingual Translation into
  Endangered Languages
Zhong Zhou Jan Niehues Alex Waibel
In many humanitarian scenarios, translation into severely low resource
languages often does not require a universal translation engine, but a
dedicated text-specific translation engine. For example, healthcare records,
hygienic procedures, government communication, emergency procedures and
religious texts are all limited texts. While generic translation engines for
all languages do not exist, translation of multilingually known limited texts
into new, endangered languages may be possible and reduce human translation
effort. We attempt to leverage translation resources from many rich resource
languages to efficiently produce best possible translation quality for a well
known text, which is available in multiple languages, in a new, severely low
resource language. We examine two approaches: 1. best selection of seed
sentences to jump start translations in a new language in view of best
generalization to the remainder of a larger targeted text(s), and 2. we adapt
large general multilingual translation engines from many other languages to
focus on a specific text in a new, unknown language. We find that adapting
large pretrained multilingual models to the domain/text first and then to the
severely low resource language works best. If we also select a best set of seed
sentences, we can improve average chrF performance on new test languages from a
baseline of 21.9 to 50.7, while reducing the number of seed sentences to only
around 1,000 in the new, unknown language.