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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 501–550 of 1773 papers

TitleStatusHype
SALTED: A Framework for SAlient Long-Tail Translation Error Detection—0
Exploiting Social Media Content for Self-Supervised Style TransferCode0
Data Augmentation to Address Out-of-Vocabulary Problem in Low-Resource Sinhala-English Neural Machine Translation—0
Controlling Formality in Low-Resource NMT with Domain Adaptation and Re-Ranking: SLT-CDT-UoS at IWSLT2022—0
AdMix: A Mixed Sample Data Augmentation Method for Neural Machine Translation—0
Training Mixed-Domain Translation Models via Federated Learning—0
The Implicit Length Bias of Label Smoothing on Beam Search Decoding—0
Jam or Cream First? Modeling Ambiguity in Neural Machine Translation with SCONES—0
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out TrainingCode0
BPE beyond Word Boundary: How NOT to use Multi Word Expressions in Neural Machine TranslationCode0
Locality-Sensitive Hashing for Long Context Neural Machine Translation—0
On Target Representation in Continuous-output Neural Machine Translation—0
Producing Standard German Subtitles for Swiss German TV Content—0
Machine Translation for Livonian: Catering to 20 Speakers—0
What Works and Doesn’t Work, A Deep Decoder for Neural Machine Translation—0
Prediction Difference Regularization against Perturbation for Neural Machine Translation—0
NVIDIA NeMo Offline Speech Translation Systems for IWSLT 2022—0
Measuring and Mitigating Name Biases in Neural Machine Translation—0
Translation Techies @DravidianLangTech-ACL2022-Machine Translation in Dravidian Languages—0
Flow-Adapter Architecture for Unsupervised Machine Translation—0
When do Contrastive Word Alignments Improve Many-to-many Neural Machine Translation?—0
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation—0
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip TranslationCode0
Dynamic Position Encoding for Transformers—0
Efficient Cluster-Based k-Nearest-Neighbor Machine TranslationCode0
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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