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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 526–550 of 1773 papers

TitleStatusHype
A Token-level Contrastive Framework for Sign Language TranslationCode0
C-NMT: A Collaborative Inference Framework for Neural Machine Translation—0
Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation—0
Data Selection Curriculum for Neural Machine Translation—0
Universal Conditional Masked Language Pre-training for Neural Machine Translation—0
Understanding and Improving Sequence-to-Sequence Pretraining for Neural Machine Translation—0
Can Synthetic Translations Improve Bitext Quality?—0
Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation—0
RMBR: A Regularized Minimum Bayes Risk Reranking Framework for Machine Translation—0
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation—0
Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise—0
Domain Adaptation in Neural Machine Translation using a Qualia-Enriched FrameNet—0
Improving English to Sinhala Neural Machine Translation using Part-of-Speech Tag—0
End-to-End Training for Back-Translation with Categorical Reparameterization TrickCode0
Data Scaling Laws in NMT: The Effect of Noise and Architecture—0
The Ecological Footprint of Neural Machine Translation SystemsCode0
Learning How to Translate North Korean through South Korean—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
Document-level Neural Machine Translation Using Dependency RST Structure—0
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation—0
Patching Leaks in the Charformer for Generative Tasks—0
On Synthetic Data for Back Translation—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints—0
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