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Towards the Next 1000 Languages in Multilingual Machine Translation: Exploring the Synergy Between Supervised and Self-Supervised Learning. (arXiv:2201.03110v2 [cs.CL] UPDATED)
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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