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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 1101–1150 of 1773 papers

TitleStatusHype
Acquiring Knowledge from Pre-trained Model to Neural Machine Translation—0
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation—0
Merging External Bilingual Pairs into Neural Machine Translation—0
Language Modelling with NMT Query Translation for Amharic-Arabic Cross-Language Information Retrieval—0
Neural Machine Translation with Explicit Phrase Alignment—0
Iterative Batch Back-Translation for Neural Machine Translation: A Conceptual Model—0
Learning to Reuse Translations: Guiding Neural Machine Translation with Examples—0
Character-based NMT with Transformer—0
Diversity by Phonetics and its Application in Neural Machine Translation—0
Translationese as a Language in "Multilingual" NMT—0
Language Model-Driven Unsupervised Neural Machine Translation—0
CCMatrix: Mining Billions of High-Quality Parallel Sentences on the WEBCode0
Domain Robustness in Neural Machine TranslationCode0
Pretrained Language Models for Document-Level Neural Machine Translation—0
A General Framework for Adaptation of Neural Machine Translation to Simultaneous Translation—0
Multi-Domain Neural Machine Translation with Word-Level Adaptive Layer-wise Domain MixingCode0
Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation—0
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding—0
Training Neural Machine Translation (NMT) Models using Tensor Train Decomposition on TensorFlow (T3F)—0
On Compositionality in Neural Machine Translation—0
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-TranslationCode0
On the Relation between Position Information and Sentence Length in Neural Machine Translation—0
On the Linguistic Representational Power of Neural Machine Translation Models—0
UCSYNLP-Lab Machine Translation Systems for WAT 2019—0
Analysing concatenation approaches to document-level NMT in two different domains—0
Recycling a Pre-trained BERT Encoder for Neural Machine Translation—0
CMU’s Machine Translation System for IWSLT 2019—0
Idiap NMT System for WAT 2019 Multimodal Translation Task—0
Combining Translation Memory with Neural Machine Translation—0
Unsupervised Neural Machine Translation with Future Rewarding—0
Hierarchical Modeling of Global Context for Document-Level Neural Machine Translation—0
Domain Adaptation of Document-Level NMT in IWSLT19—0
Sentiment Aware Neural Machine Translation—0
OPPO NMT System for IWSLT 2019—0
Benefits of Data Augmentation for NMT-based Text Normalization of User-Generated Content—0
Improving Neural Machine Translation by Achieving Knowledge Transfer with Sentence Alignment Learning—0
English to Hindi Multi-modal Neural Machine Translation and Hindi Image Captioning—0
English-Myanmar Supervised and Unsupervised NMT: NICT's Machine Translation Systems at WAT-2019—0
LTRC-MT Simple \& Effective Hindi-English Neural Machine Translation Systems at WAT 2019—0
Empirical Evaluation of Active Learning Techniques for Neural MT—0
Efficient Bilingual Generalization from Neural Transduction Grammar Induction—0
Learning to Generate Word- and Phrase-Embeddings for Efficient Phrase-Based Neural Machine Translation—0
Long Warm-up and Self-Training: Training Strategies of NICT-2 NMT System at WAT-2019—0
Lexical Micro-adaptation for Neural Machine Translation—0
Japanese-Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019—0
Controlling Japanese Honorifics in English-to-Japanese Neural Machine Translation—0
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English—0
Zero-Resource Neural Machine Translation with Monolingual Pivot Data—0
Synchronously Generating Two Languages with Interactive Decoding—0
Data augmentation using back-translation for context-aware neural machine translation—0
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