  While there are more than 7000 languages in the world, most translation
research efforts have targeted a few high-resource languages. Commercial
translation systems support only one hundred languages or fewer, and do not
make these models available for transfer to low resource languages. In this
work, we present useful tools for machine translation research: MTData,
NLCodec, and RTG. We demonstrate their usefulness by creating a multilingual
neural machine translation model capable of translating from 500 source
languages to English. We make this multilingual model readily downloadable and
usable as a service, or as a parent model for transfer-learning to even
lower-resource languages.
