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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 1526–1550 of 1773 papers

TitleStatusHype
Phrase Table as Recommendation Memory for Neural Machine Translation—0
Meta-Learning for Low-Resource Neural Machine Translation—0
Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training—0
Sparse and Constrained Attention for Neural Machine TranslationCode0
Combining Advanced Methods in Japanese-Vietnamese Neural Machine TranslationCode0
Are BLEU and Meaning Representation in Opposition?—0
Towards Robust Neural Machine Translation—0
Triangular Architecture for Rare Language Translation—0
First Experiments with Neural Translation of Informal to Formal Mathematics—0
Deep Neural Machine Translation with Weakly-Recurrent UnitsCode0
Character-level Chinese-English Translation through ASCII EncodingCode0
Neural Machine Translation Decoding with Terminology Constraints—0
Improving Character-level Japanese-Chinese Neural Machine Translation with Radicals as an Additional Input Feature—0
Stay On-Topic: Generating Context-specific Fake Restaurant ReviewsCode0
Multi-Domain Neural Machine Translation—0
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation—0
Exploring Hyper-Parameter Optimization for Neural Machine Translation on GPU Architectures—0
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character LevelCode0
KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction—0
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT—0
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation—0
A fine-grained error analysis of NMT, SMT and RBMT output for English-to-Dutch—0
Training and Adapting Multilingual NMT for Less-resourced and Morphologically Rich LanguagesCode0
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment—0
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword CandidatesCode0
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