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NMT

Neural machine translation is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Papers

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Showing 1751–1773 of 1773 papers

TitleStatusHype
Neural Name Translation Improves Neural Machine Translation—0
Guided Alignment Training for Topic-Aware Neural Machine TranslationCode0
Compression of Neural Machine Translation Models via PruningCode0
Sequence-Level Knowledge DistillationCode1
The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16—0
Semi-Supervised Learning for Neural Machine Translation—0
Deep Recurrent Models with Fast-Forward Connections for Neural Machine TranslationCode0
Memory-enhanced Decoder for Neural Machine Translation—0
Incorporating Discrete Translation Lexicons into Neural Machine TranslationCode0
The AMU-UEDIN Submission to the WMT16 News Translation Task: Attention-based NMT Models as Feature Functions in Phrase-based SMTCode0
Syntactically Guided Neural Machine Translation—0
Coverage Embedding Models for Neural Machine Translation—0
Transfer Learning for Low-Resource Neural Machine TranslationCode0
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word-Character ModelsCode0
Tree-to-Sequence Attentional Neural Machine TranslationCode0
Modeling Coverage for Neural Machine TranslationCode0
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model—0
Stanford Neural Machine Translation Systems for Spoken Language DomainsCode0
Improving Neural Machine Translation Models with Monolingual DataCode1
Neural Machine Translation of Rare Words with Subword UnitsCode1
Effective Approaches to Attention-based Neural Machine TranslationCode1
Addressing the Rare Word Problem in Neural Machine TranslationCode0
On the Relationship between Neural Machine Translation and Word Alignment—0
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