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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 1701–1725 of 1773 papers

TitleStatusHype
Incorporating Global Visual Features into Attention-Based Neural Machine Translation—0
QCRI Machine Translation Systems for IWSLT 16—0
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation—0
Towards Decoding as Continuous Optimization in Neural Machine Translation—0
OpenNMT: Open-Source Toolkit for Neural Machine TranslationCode0
Neural Machine Translation on Scarce-Resource Condition: A case-study on Persian-English—0
Joint Prediction of Word Alignment with Alignment Types—0
Fast Domain Adaptation for Neural Machine Translation—0
Domain specialization: a post-training domain adaptation for Neural Machine Translation—0
Domain Control for Neural Machine Translation—0
Boosting Neural Machine Translation—0
Neural Machine Translation from Simplified Translations—0
How Grammatical is Character-level Neural Machine Translation? Assessing MT Quality with Contrastive Translation PairsCode0
Neural Machine Translation by Minimising the Bayes-risk with Respect to Syntactic Translation Lattices—0
What Makes Word-level Neural Machine Translation Hard: A Case Study on English-German Translation—0
QCRI’s Machine Translation Systems for IWSLT’16—0
Kyoto-NMT: a Neural Machine Translation implementation in ChainerCode0
FBK’s Neural Machine Translation Systems for IWSLT 2016—0
A study of attention-based neural machine translation model on Indian languages—0
Faster and Lighter Phrase-based Machine Translation Baseline—0
A Character-Aware Encoder for Neural Machine Translation—0
Adaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016—0
Factored Neural Machine Translation Architectures—0
Ensemble Learning for Multi-Source Neural Machine Translation—0
UFAL Submissions to the IWSLT 2016 MT Track—0
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