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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 1051–1100 of 1773 papers

TitleStatusHype
Source and Target Bidirectional Knowledge Distillation for End-to-end Speech Translation—0
SOURCE: SOURce-Conditional Elmo-style Model for Machine Translation Quality Estimation—0
Sparse and Decorrelated Representations for Stable Zero-shot NMT—0
Sparse Persistent RNNs: Squeezing Large Recurrent Networks On-Chip—0
Specializing Multi-domain NMT via Penalizing Low Mutual Information—0
Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary—0
Spiral Language Modeling—0
SSN-NLP at SemEval-2020 Task 4: Text Classification and Generation on Common Sense Context Using Neural Networks—0
Start-Before-End and End-to-End: Neural Speech Translation by AppTek and RWTH Aachen University—0
Studying The Impact Of Document-level Context On Simultaneous Neural Machine Translation—0
Style Transfer as Unsupervised Machine Translation—0
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding—0
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags—0
Supervised and Unsupervised Machine Translation for Myanmar-English and Khmer-English—0
Switching-Aligned-Words Data Augmentation for Neural Machine Translation—0
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation—0
Synchronously Generating Two Languages with Interactive Decoding—0
Syntactically Guided Neural Machine Translation—0
Syntax-Aware Complex-Valued Neural Machine Translation—0
Syntax-aware Data Augmentation for Neural Machine Translation—0
Syntax-Directed Attention for Neural Machine Translation—0
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations—0
Syntax in End-to-End Natural Language Processing—0
Synthesizing Monolingual Data for Neural Machine Translation—0
Synthetic Pre-Training Tasks for Neural Machine Translation—0
Synthetic Source Language Augmentation for Colloquial Neural Machine Translation—0
SYSTRAN's Pure Neural Machine Translation Systems—0
Tagged Back-Translation—0
Tagged Back-translation Revisited: Why Does It Really Work?—0
Tag-less Back-Translation—0
Tailoring Neural Architectures for Translating from Morphologically Rich Languages—0
Taking Actions Separately: A Bidirectionally-Adaptive Transfer Learning Method for Low-Resource Neural Machine Translation—0
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation—0
Target Conditioning for One-to-Many Generation—0
A Targeted Attack on Black-Box Neural Machine Translation with Parallel Data Poisoning—0
Temporal Attention Model for Neural Machine Translation—0
Tencent AI Lab Machine Translation Systems for WMT20 Chat Translation Task—0
TencentFmRD Neural Machine Translation for WMT18—0
Terminology-Aware Segmentation and Domain Feature for the WMT19 Biomedical Translation Task—0
Terminology-Aware Sentence Mining for NMT Domain Adaptation: ADAPT’s Submission to the Adap-MT 2020 English-to-Hindi AI Translation Shared Task—0
Testing Untestable Neural Machine Translation: An Industrial Case—0
Text2Sign: Towards Sign Language Production Using Neural Machine Translation and Generative Adversarial Networks—0
APE at Scale and its Implications on MT Evaluation Biases—0
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili—0
The ADAPT’s Submissions to the WMT20 Biomedical Translation Task—0
The ADAPT System Description for the WMT20 News Translation Task—0
The Benefit of Pseudo-Reference Translations in Quality Estimation of MT Output—0
The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations—0
The Edit Distance Transducer in Action: The University of Cambridge English-German System at WMT16—0
The Helsinki submission to the AmericasNLP shared task—0
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