Train Global, Tailor Local: Minimalist Multilingual Translation into Endangered Languages
This addresses humanitarian translation needs for specific texts like healthcare or emergency procedures in endangered languages, though it is incremental as it builds on existing multilingual models.
The paper tackles the problem of translating limited texts into severely low-resource endangered languages by adapting large multilingual models and selecting optimal seed sentences, improving average chrF performance from 21.9 to 50.7 while reducing required seed sentences to about 1,000.
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.