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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 1551–1600 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 Translation—0
Guiding Neural Machine Translation with Retrieved Translation Pieces—0
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts—0
Fine-Grained Attention Mechanism for Neural Machine Translation—0
Training, feedback and productivity measurement with NMT and Adaptive MT—0
SMT versus NMT: Preliminary comparisons for Irish—0
Turning NMT Research into Commercial Products—0
Joint Training for Neural Machine Translation Models with Monolingual Data—0
XNMT: The eXtensible Neural Machine Translation ToolkitCode1
Gender Aware Spoken Language Translation Applied to English-Arabic—0
Examining the Tip of the Iceberg: A Data Set for Idiom TranslationCode0
Zero-Resource Neural Machine Translation with Multi-Agent Communication Game—0
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation—0
Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging TasksCode1
Variational Recurrent Neural Machine Translation—0
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 Prediction—0
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-Edits—0
Multi-channel Encoder for Neural Machine Translation—0
Neural Machine Translation by Generating Multiple Linguistic Factors—0
KIT’s Multilingual Neural Machine Translation systems for IWSLT 2017—0
The RWTH Aachen Machine Translation Systems for IWSLT 2017—0
Kyoto University MT System Description for IWSLT 2017—0
Monolingual Embeddings for Low Resourced Neural Machine TranslationCode0
Evolution Strategy Based Automatic Tuning of Neural Machine Translation Systems—0
Towards better translation performance on spoken language—0
Decoding with Value Networks for Neural Machine Translation—0
Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches—0
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 Translation—0
Synthetic and Natural Noise Both Break Neural Machine TranslationCode0
Towards Neural Machine Translation with Partially Aligned Corpora—0
Understanding and Improving Morphological Learning in the Neural Machine Translation Decoder—0
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project—0
XMU Neural Machine Translation Online Service—0
Key-value Attention Mechanism for Neural Machine Translation—0
Context-Aware Smoothing for Neural Machine Translation—0
Concept Equalization to Guide Correct Training of Neural Machine Translation—0
Neural Machine Translation: Basics, Practical Aspects and Recent Trends—0
Neural Lattice Search for Domain Adaptation in Machine Translation—0
CKY-based Convolutional Attention for Neural Machine Translation—0
Patent NMT integrated with Large Vocabulary Phrase Translation by SMT at WAT 2017—0
Tokyo Metropolitan University Neural Machine Translation System for WAT 2017—0
Improving Japanese-to-English Neural Machine Translation by Paraphrasing the Target Language—0
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