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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 926–950 of 1773 papers

TitleStatusHype
Pre-training via Leveraging Assisting Languages for Neural Machine Translation—0
Pre-Translation for Neural Machine Translation—0
Priming Ancient Korean Neural Machine Translation—0
Priming Neural Machine Translation—0
Probing Causes of Hallucinations in Neural Machine Translations—0
Producing Standard German Subtitles for Swiss German TV Content—0
Product Review Translation: Parallel Corpus Creation and Robustness towards User-generated Noisy Text—0
Product Review Translation using Phrase Replacement and Attention Guided Noise Augmentation—0
Promoting Target Data in Context-aware Neural Machine Translation—0
Promoting the Knowledge of Source Syntax in Transformer NMT Is Not Needed—0
Prompting Neural Machine Translation with Translation Memories—0
Proverbs Run in Pairs: Evaluating Proverb Translation Capability of Large Language Model—0
Pseudo-Label Training and Model Inertia in Neural Machine Translation—0
Punjabi to English Bidirectional NMT System—0
PyMarian: Fast Neural Machine Translation and Evaluation in Python—0
QCRI Machine Translation Systems for IWSLT 16—0
QCRI’s Machine Translation Systems for IWSLT’16—0
QE-EBM: Using Quality Estimators as Energy Loss for Machine Translation—0
Quality-Aware Decoding for Neural Machine Translation—0
Quality-Aware Decoding: Unifying Quality Estimation and Decoding—0
Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model—0
Quality Estimation based Feedback Training for Improving Pronoun Translation—0
Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation—0
Quantitative Analysis of Post-Editing Effort Indicators for NMT—0
Quantity vs. Quality of Monolingual Source Data in Automatic Text Translation: Can It Be Too Little If It Is Too Good?—0
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