Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

Naveen Arivazhagan; Ankur Bapna; Orhan Firat; Dmitry Lepikhin; Melvin Johnson; Maxim Krikun; Mia Xu Chen; Yuan Cao; George Foster; Colin Cherry; Wolfgang Macherey; Zhifeng Chen; Yonghui Wu

arXiv · 1907.05019

Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges

Naveen Arivazhagan·Ankur Bapna·Orhan Firat·Dmitry Lepikhin·Melvin Johnson·Maxim Krikun·Mia Xu Chen·Yuan Cao·George Foster·Colin Cherry·Wolfgang Macherey·Zhifeng Chen·Yonghui Wu

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Abstract

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.

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Naveen Arivazhagan, Ankur Bapna, Orhan Firat, Dmitry Lepikhin, Melvin Johnson, Maxim Krikun, Mia Xu Chen, Yuan Cao, George Foster, Colin Cherry, Wolfgang Macherey, Zhifeng Chen, Yonghui Wu. 2019-07-11. Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges. https://arxiv.org/abs/1907.05019

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