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Multilingual Neural Machine Translation (MNMT) enables one system to translate sentences from multiple source languages to multiple target languages, greatly reducing deployment costs compared with conventional bilingual systems.
A comparison of pivot methods for phrase-based statistical machine translation
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Investigating multilingual NMT representations at scale
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Complete multilingual neural machine translation
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COMET: A neural framework for MT evaluation
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On negative interference in multilingual models: Findings and a meta-learning treatment
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Improving massively multilingual neural machine translation and zero-shot translation
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Deep encoder, shallow decoder: Reevaluating non-autoregressive machine translation
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An efficient transformer decoder with compressed sub-layers
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Findings of the wmt 2021 shared task on large-scale multilingual machine translation
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On the variance of the adaptive learning rate and beyond
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Multilingual machine translation systems from Microsoft for WMT21 shared task
Jian Yang, Shuming Ma, Haoyang Huang, Dongdong Zhang, Li Dong, Shaohan Huang, Alexandre Muzio, Saksham Singhal, Hany Hassan, Xia Song, and Furu Wei. 2021a
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Guillaume Wenzek, Vishrav Chaudhary, Angela Fan, Sahir Gomez, Naman Goyal, Somya Jain, Douwe Kiela, Tristan Thrush, and Francisco Guzmán. 2021 · 2021
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Improving multilingual translation by representation and gradient regularization
Yilin Yang, Akiko Eriguchi, Alexandre Muzio, Prasad Tadepalli, Stefan Lee, and Hany Hassan. 2021b · 2021
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