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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 551–575 of 1773 papers

TitleStatusHype
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
On Synthetic Data for Back Translation—0
Nearest Neighbor Knowledge Distillation for Neural Machine Translation—0
Cost-Effective Training in Low-Resource Neural Machine Translation—0
PAEG: Phrase-level Adversarial Example Generation for Neural Machine Translation—0
SMDT: Selective Memory-Augmented Neural Document Translation—0
How do lexical semantics affect translation? An empirical studyCode0
ViNMT: Neural Machine Translation ToolkitCode0
Frequency-Aware Contrastive Learning for Neural Machine Translation—0
A Preordered RNN Layer Boosts Neural Machine Translation in Low Resource Settings—0
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models—0
Spiral Language Modeling—0
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource LanguagesCode0
Amortized Noisy Channel Neural Machine Translation—0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
Communication-Efficient Federated Learning for Neural Machine Translation—0
Skeletal Graph Self-Attention: Embedding a Skeleton Inductive Bias into Sign Language Production—0
EduMT: Developing Machine Translation System for Educational Content in Indian Languages—0
Named Entity-Factored Transformer for Proper Noun Translation—0
Are Ellipses Important for Machine Translation?—0
Improvement in Machine Translation with Generative Adversarial Networks—0
Deps-SAN: Neural Machine Translation with Dependency-Scaled Self-Attention Network—0
Causal Transformers: Improving the Robustness on Spurious Correlations—0
IndicBART: A Pre-trained Model for Indic Natural Language Generation—0
A Primer in NMTology: What we have understood about NMT—0
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