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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 15511575 of 1773 papers

TitleStatusHype
Can Neural Machine Translation be Improved with User Feedback?0
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation0
Guiding Neural Machine Translation with Retrieved Translation Pieces0
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts0
Fine-Grained Attention Mechanism for Neural Machine Translation0
Training, feedback and productivity measurement with NMT and Adaptive MT0
SMT versus NMT: Preliminary comparisons for Irish0
Turning NMT Research into Commercial Products0
Joint Training for Neural Machine Translation Models with Monolingual Data0
XNMT: The eXtensible Neural Machine Translation ToolkitCode1
Gender Aware Spoken Language Translation Applied to English-Arabic0
Examining the Tip of the Iceberg: A Data Set for Idiom TranslationCode0
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game0
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation0
Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging TasksCode1
Variational Recurrent Neural Machine Translation0
Asynchronous Bidirectional Decoding for Neural Machine TranslationCode1
What Level of Quality can Neural Machine Translation Attain on Literary Text?0
Improved English to Russian Translation by Neural Suffix Prediction0
Translating Pro-Drop Languages with Reconstruction ModelsCode0
Sockeye: A Toolkit for Neural Machine TranslationCode0
A User-Study on Online Adaptation of Neural Machine Translation to Human Post-Edits0
Multi-channel Encoder for Neural Machine Translation0
Neural Machine Translation by Generating Multiple Linguistic Factors0
KIT’s Multilingual Neural Machine Translation systems for IWSLT 20170
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