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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 1501–1550 of 1773 papers

TitleStatusHype
Deconvolution-Based Global Decoding for Neural Machine TranslationCode0
Multilingual Neural Machine Translation with Task-Specific Attention—0
Multi-Source Neural Machine Translation with Missing Data—0
How Do Source-side Monolingual Word Embeddings Impact Neural Machine Translation?—0
Dense Information Flow for Neural Machine TranslationCode0
Neural Sign Language TranslationCode0
Meaningless yet meaningful: Morphology grounded subword-level NMT—0
Morphological Word Embeddings for Arabic Neural Machine Translation in Low-Resource Settings—0
A Survey of Domain Adaptation for Neural Machine Translation—0
Bi-Directional Neural Machine Translation with Synthetic Parallel Data—0
OpenNMT: Neural Machine Translation ToolkitCode1
Inducing Grammars with and for Neural Machine Translation—0
Reliability and Learnability of Human Bandit Feedback for Sequence-to-Sequence Reinforcement LearningCode0
Zero-Shot Dual Machine TranslationCode0
Phrase Table as Recommendation Memory for Neural Machine Translation—0
Japanese Predicate Conjugation 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
Deep Neural Machine Translation with Weakly-Recurrent UnitsCode0
First Experiments with Neural Translation of Informal to Formal Mathematics—0
Neural Machine Translation Decoding with Terminology Constraints—0
Character-level Chinese-English Translation through ASCII EncodingCode0
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
Exploring Hyper-Parameter Optimization for Neural Machine Translation on GPU Architectures—0
Compositional Representation of Morphologically-Rich Input for Neural Machine Translation—0
Incorporating Chinese Radicals Into Neural Machine Translation: Deeper Than Character LevelCode0
KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction—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
Dynamic Sentence Sampling for Efficient Training of Neural Machine Translation—0
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT—0
Accelerating NMT Batched Beam Decoding with LMBR Posteriors for Deployment—0
Subword Regularization: Improving Neural Network Translation Models with Multiple Subword CandidatesCode0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip—0
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language InferenceCode1
Unsupervised Neural Machine Translation with Weight SharingCode0
A neural interlingua for multilingual machine translation—0
Phrase-Based & Neural Unsupervised Machine TranslationCode0
Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation—0
When and Why are Pre-trained Word Embeddings Useful for Neural Machine Translation?Code0
Investigating Backtranslation in Neural Machine Translation—0
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation—0
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