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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 451–500 of 1773 papers

TitleStatusHype
English-Russian Data Augmentation for Neural Machine Translation—0
Discourse Cohesion Evaluation for Document-Level Neural Machine Translation—0
How Effective is Byte Pair Encoding for Out-Of-Vocabulary Words in Neural Machine Translation?—0
Benchmarking Azerbaijani Neural Machine Translation—0
Multimodal Neural Machine Translation with Search Engine Based Image Retrieval—0
Learning to translate by learning to communicateCode0
Sockeye 3: Fast Neural Machine Translation with PyTorch—0
UM4: Unified Multilingual Multiple Teacher-Student Model for Zero-Resource Neural Machine TranslationCode0
Bridging the Gap between Training and Inference: Multi-Candidate Optimization for Diverse Neural Machine TranslationCode0
End-to-End Simultaneous Speech Translation with Pretraining and Distillation: Huawei Noah’s System for AutoSimTranS 2022—0
Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource LanguageCode0
Reducing Disambiguation Biases in NMT by Leveraging Explicit Word Sense Information—0
Unified NMT models for the Indian subcontinent, transcending script-barriers—0
Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence TransformersCode0
Reduce Indonesian Vocabularies with an Indonesian Sub-word Separator—0
Code Translation with Compiler Representations—0
Probing Causes of Hallucinations in Neural Machine Translations—0
FreeTransfer-X: Safe and Label-Free Cross-Lingual Transfer from Off-the-Shelf Models—0
Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages—0
Finetuning a Kalaallisut-English machine translation system using web-crawled data—0
Finding the Right Recipe for Low Resource Domain Adaptation in Neural Machine Translation—0
Introducing EM-FT for Manipuri-English Neural Machine Translation—0
Using Semantic Role Labeling to Improve Neural Machine Translation—0
Dynamic Adaptation of Neural Machine-Translation Systems Through Translation Exemplars—0
Passing Parser Uncertainty to the Transformer. Labeled Dependency Distributions for Neural Machine Translation.—0
Machine Translation of 16Th Century Letters from Latin to German—0
Achievements of the PRINCIPLE Project: Promoting MT for Croatian, Icelandic, Irish and Norwegian—0
Latest Development in the FoTran Project – Scaling Up Language Coverage in Neural Machine Translation Using Distributed Training with Language-Specific Components—0
InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation—0
BERTology for Machine Translation: What BERT Knows about Linguistic Difficulties for Translation—0
Fast-Paced Improvements to Named Entity Handling for Neural Machine Translation—0
The Multilingual Microblog Translation Corpus: Improving and Evaluating Translation of User-Generated Text—0
MultitraiNMT Erasmus+ project: Machine Translation Training for multilingual citizens (multitrainmt.eu)—0
Priming Ancient Korean Neural Machine Translation—0
Automatic Bilingual Phrase Dictionary Construction from GIZA++ Output—0
CREAMT: Creativity and narrative engagement of literary texts translated by translators and NMT—0
Evaluating Pre-training Objectives for Low-Resource Translation into Morphologically Rich Languages—0
Low-resource Neural Machine Translation: Benchmarking State-of-the-art Transformer for Wolof<->French—0
Pre-training Synthetic Cross-lingual Decoder for Multilingual Samples Adaptation in E-Commerce Neural Machine Translation—0
A Benchmark Dataset for Multi-Level Complexity-Controllable Machine TranslationCode0
A Systematic Study Reveals Unexpected Interactions in Pre-Trained Neural Machine Translation—0
Multilingual Neural Machine Translation With the Right Amount of Sharing—0
Comparing Multilingual NMT Models and Pivoting—0
Comparing and combining tagging with different decoding algorithms for back-translation in NMT: learnings from a low resource scenario—0
Introducing the CURLICAT Corpora: Seven-language Domain Specific Annotated Corpora from Curated Sources—0
Challenges of Neural Machine Translation for Short Texts—0
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine TranslationCode0
Patching Leaks in the Charformer for Efficient Character-Level GenerationCode0
Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages—0
DivEMT: Neural Machine Translation Post-Editing Effort Across Typologically Diverse LanguagesCode0
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