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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 651–675 of 1773 papers

TitleStatusHype
Dependency-Based Self-Attention for Transformer NMT—0
Dependency-Based Relative Positional Encoding for Transformer NMT—0
Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection—0
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation—0
Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages—0
Defining Boundaries: The Impact of Domain Specification on Cross-Language and Cross-Domain Transfer in Machine Translation—0
Beyond BLEU:Training Neural Machine Translation with Semantic Similarity—0
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture—0
An Empirical Study of Document-to-document Neural Machine Translation—0
Deep Neural Machine Translation with Linear Associative Unit—0
Decoding with Value Networks for Neural Machine Translation—0
Better OOV Translation with Bilingual Terminology Mining—0
Addressing Troublesome Words in Neural Machine Translation—0
A Comprehensive Survey of Multilingual Neural Machine Translation—0
Decoding-History-Based Adaptive Control of Attention for Neural Machine Translation—0
Decoding and Diversity in Machine Translation—0
Better Datastore, Better Translation: Generating Datastores from Pre-Trained Models for Nearest Neural Machine Translation—0
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation—0
An Empirical Investigation of Multi-bridge Multilingual NMT models—0
Debiasing Word Embeddings Improves Multimodal Machine Translation—0
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding—0
Data Weighted Training Strategies for Grammatical Error Correction—0
Data Selection with Feature Decay Algorithms Using an Approximated Target Side—0
BERT-JAM: Boosting BERT-Enhanced Neural Machine Translation with Joint Attention—0
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation—0
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