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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 401–450 of 1773 papers

TitleStatusHype
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip TranslationCode0
Dynamic Position Encoding for Transformers—0
Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine TranslationCode1
Efficient Cluster-Based k-Nearest-Neighbor Machine TranslationCode0
A Token-level Contrastive Framework for Sign Language TranslationCode0
Towards Better Chinese-centric Neural Machine Translation for Low-resource LanguagesCode1
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
Integrating Vectorized Lexical Constraints for Neural Machine TranslationCode1
Learning Confidence for Transformer-based Neural Machine TranslationCode1
Neural Machine Translation with Phrase-Level Universal Visual RepresentationsCode1
Universal Conditional Masked Language Pre-training for Neural Machine TranslationCode0
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
Can We Generate Shellcodes via Natural Language? An Empirical StudyCode1
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
Linguistically-driven Multi-task Pre-training for Low-resource Neural Machine TranslationCode1
Document-level Neural Machine Translation Using Dependency RST Structure—0
On Synthetic Data for Back Translation—0
What Role Does BERT Play in the Neural Machine Translation Encoder?—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
Nearest Neighbor Knowledge Distillation for Neural Machine Translation—0
Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation—0
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints—0
Knowledge Based Template Machine Translation In Low-Resource Setting—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
Patching Leaks in the Charformer for Generative Tasks—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
Amortized Noisy Channel Neural Machine Translation—0
Harnessing Cross-lingual Features to Improve Cognate Detection for Low-resource LanguagesCode0
Improving Both Domain Robustness and Domain Adaptability in Machine TranslationCode0
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