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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 1501–1525 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
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