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, significantly
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.

我们致力于构建一款通用的神经机器翻译系统，通过构建一个单一的大规模多语言 NMT 模型，实现了 103 种语言之间的翻译，带有有效的迁移学习能力，显着提高了低资源语言的翻译质量，同时保持高资源语言翻译质量与竞争双语基线相当，为实现通用 NMT 模型的质量和实用性提供了多个方面的模型构建分析，并指出未来研究的方向和需进一步解决的问题。