Massively Multilingual Text Translation For Low-Resource Languages
Zhong Zhou
Translation into severely low-resource languages has both the cultural goal
of saving and reviving those languages and the humanitarian goal of assisting
the everyday needs of local communities that are accelerated by the recent
COVID-19 pandemic. In many humanitarian efforts, 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, low-resource languages may be possible and reduce human
translation effort. We attempt to leverage translation resources from
rich-resource languages to efficiently produce best possible translation
quality for well known texts, which are available in multiple languages, in a
new, low-resource language. To reach this goal, we argue that in translating a
closed text into low-resource languages, generalization to out-of-domain texts
is not necessary, but generalization to new languages is. Performance gain
comes from massive source parallelism by careful choice of close-by language
families, style-consistent corpus-level paraphrases within the same language
and strategic adaptation of existing large pretrained multilingual models to
the domain first and then to the language. Such performance gain makes it
possible for machine translation systems to collaborate with human translators
to expedite the translation process into new, low-resource languages.