Jan. 14, 2022, 2:11 a.m. | Aditya Siddhant, Ankur Bapna, Orhan Firat, Yuan Cao, Mia Xu Chen, Isaac Caswell, Xavier Garcia

cs.LG updates on arXiv.org arxiv.org

Achieving universal translation between all human language pairs is the
holy-grail of machine translation (MT) research. While recent progress in
massively multilingual MT is one step closer to reaching this goal, it is
becoming evident that extending a multilingual MT system simply by training on
more parallel data is unscalable, since the availability of labeled data for
low-resource and non-English-centric language pairs is forbiddingly limited. To
this end, we present a pragmatic approach towards building a multilingual MT
model that …

arxiv learning machine machine translation self-supervised learning supervised learning translation

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