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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

Showing 15761600 of 1773 papers

TitleStatusHype
The RWTH Aachen Machine Translation Systems for IWSLT 20170
Kyoto University MT System Description for IWSLT 20170
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems0
Towards better translation performance on spoken language0
Decoding with Value Networks for Neural Machine Translation0
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches0
Modeling Past and Future for Neural Machine TranslationCode0
Learning to Remember Translation History with a Continuous CacheCode0
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMTCode1
Syntax-Directed Attention for Neural Machine Translation0
Synthetic and Natural Noise Both Break Neural Machine TranslationCode0
Towards Neural Machine Translation with Partially Aligned Corpora0
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder0
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project0
XMU Neural Machine Translation Online Service0
Key-value Attention Mechanism for Neural Machine Translation0
Context-Aware Smoothing for Neural Machine Translation0
Concept Equalization to Guide Correct Training of Neural Machine Translation0
Neural Machine Translation: Basics, Practical Aspects and Recent Trends0
Neural Lattice Search for Domain Adaptation in Machine Translation0
CKY-based Convolutional Attention for Neural Machine Translation0
Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 20170
Tokyo Metropolitan University Neural Machine Translation System for WAT 20170
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language0
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