“Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges”, 2019-07-11 (; similar):
We introduce our efforts towards building a universal neural machine translation (NMT) system capable of translating between any language pair.
We set a milestone towards this goal by building a single massively multilingual NMT model handling 103 languages trained on over 25 billion examples.
Our system demonstrates effective transfer learning ability, improving translation quality of low-resource languages, while keeping high-resource language translation quality on-par with competitive bilingual baselines.
We provide in-depth analysis of various aspects of model building that are crucial to achieving quality and practicality in universal NMT.
While we prototype a high-quality universal translation system, our extensive empirical analysis exposes issues that need to be further addressed, and we suggest directions for future research.