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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 476–500 of 1773 papers

TitleStatusHype
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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