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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
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip—0
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
Near Human-Level Performance in Grammatical Error Correction with Hybrid Machine Translation—0
Can Neural Machine Translation be Improved with User Feedback?—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
Joint Training for Neural Machine Translation Models with Monolingual Data—0
Turning NMT Research into Commercial Products—0
Training, feedback and productivity measurement with NMT and Adaptive MT—0
SMT versus NMT: Preliminary comparisons for Irish—0
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
Variational Recurrent Neural Machine Translation—0
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
The RWTH Aachen Machine Translation Systems 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
KIT’s Multilingual Neural Machine Translation systems for IWSLT 2017—0
Kyoto University MT System Description for IWSLT 2017—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
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
Comparison of SMT and NMT trained with large Patent Corpora: Japio at WAT2017—0
Neural Machine Translation: Basics, Practical Aspects and Recent Trends—0
Neural Lattice Search for Domain Adaptation in Machine Translation—0
XMU Neural Machine Translation Online Service—0
Evaluating Discourse Phenomena in Neural Machine Translation—0
SMT reranked NMT—0
NMT or SMT: Case Study of a Narrow-domain English-Latvian Post-editing Project—0
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