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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 626–650 of 1773 papers

TitleStatusHype
Sicilian Translator: A Recipe for Low-Resource NMTCode0
Improving Zero-shot Multilingual Neural Machine Translation for Low-Resource Languages—0
Prose2Poem: The Blessing of Transformers in Translating Prose to Persian PoetryCode0
EdinSaar@WMT21: North-Germanic Low-Resource Multilingual NMT—0
TransSlowDown: Efficiency Attacks on Neural Machine Translation Systems—0
On the Effectiveness of Quasi Character-Level Models for Machine Translation—0
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer—0
Faithful Target Attribute Prediction in Neural Machine TranslationCode0
Non-Parametric Online Learning from Human Feedback for Neural Machine TranslationCode0
Learning Domain Specific Language Models for Automatic Speech Recognition through Machine Translation—0
Digging Errors in NMT: Evaluating and Understanding Model Errors from Hypothesis Distribution—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Towards Full Utilization on Mask Task for Distilling PLMs into NMT—0
Modeling Future for Neural Machine Translation by Fusing Target Information—0
Modeling Multi-granularity Segmentation for Rare Words in Neural Machine Translation—0
Translation Transformers Rediscover Inherent Data DomainsCode0
Scaling Laws for Neural Machine Translation—0
Efficient Inference for Multilingual Neural Machine Translation—0
Contrastive Learning for Context-aware Neural Machine TranslationUsing Coreference Information—0
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions—0
Multi-Sentence Resampling: A Simple Approach to Alleviate Dataset Length Bias and Beam-Search DegradationCode0
Fine Grained Human Evaluation for English-to-Chinese Machine Translation: A Case Study on Scientific Text—0
Modeling Concentrated Cross-Attention for Neural Machine Translation with Gaussian Mixture Model—0
Rule-based Morphological Inflection Improves Neural Terminology TranslationCode0
Neural Machine Translation Quality and Post-Editing PerformanceCode0
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