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Papers-on-Neural-Machine-Translation

Transformer & Variants

Attention Is All You Need

The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation

Accelerating Neural Transformer via an Average Attention Network

Unsupervised NMT

Unsupervised Neural Machine Translation Using Monolingual Corpora Only

Unsupervised Pretraining for Sequence to Sequence Learning

Unsupervised Neural Machine Translation

Phrase-Based & Neural Unsupervised Machine Translation

Transfer learning / Zero-shot NMT / Monolingual data

A Survey of Domain Adaptation for Neural Machine Translation

Zero-shot translation

Transfer Learning for Low-Resource Neural Machine Translation

On Using Monolingual Corpora in Neural Machine Translation

Improving Neural Machine Translation Models with Monolingual Data

Exploiting Source-side Monolingual Data in Neural Machine Translation

Joint Training for Neural Machine Translation Models with Monolingual Data

Effective Domain Mixing for Neural Machine Translation

Dual learning NMT

Dual learning for Machine Translation

Achieving Human Parity on Automatic Chinese to English News Translation

Lower-source NMT

Universal Neural Machine Translation for Extremely Low Resource Languages

Others

Adversarial Neural Machine Translation

Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets

Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization

Learning principled bilingual mappings of word embeddings while preserving monolingual invariance

Learning bilingual word embeddings with (almost) no bilingual data

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