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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 1026–1050 of 1773 papers

TitleStatusHype
Reinforced Curriculum Learning on Pre-trained Neural Machine Translation Models—0
Neural Machine Translation: Challenges, Progress and FutureCode1
An In-depth Walkthrough on Evolution of Neural Machine Translation—0
On Optimal Transformer Depth for Low-Resource Language TranslationCode0
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation ProblemCode1
Explicit Reordering for Neural Machine Translation—0
Dynamic Data Selection and Weighting for Iterative Back-TranslationCode0
Cross-lingual Supervision Improves Unsupervised Neural Machine Translation—0
Dictionary-based Data Augmentation for Cross-Domain Neural Machine Translation—0
Meta-Learning for Few-Shot NMT Adaptation—0
Understanding Learning Dynamics for Neural Machine Translation—0
Finding the Optimal Vocabulary Size for Neural Machine TranslationCode0
AR: Auto-Repair the Synthetic Data for Neural Machine Translation—0
DeepSumm -- Deep Code Summaries using Neural Transformer Architecture—0
Low Resource Neural Machine Translation: A Benchmark for Five African LanguagesCode1
Learning Contextualized Sentence Representations for Document-Level Neural Machine Translation—0
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian PidginCode0
Towards Neural Machine Translation for Edoid Languages—0
Capturing document context inside sentence-level neural machine translation models with self-training—0
Evaluating Low-Resource Machine Translation between Chinese and Vietnamese with Back-Translation—0
Modeling Future Cost for Neural Machine Translation—0
Do all Roads Lead to Rome? Understanding the Role of Initialization in Iterative Back-Translation—0
Echo State Neural Machine Translation—0
MuST-Cinema: a Speech-to-Subtitles corpus—0
Guider l'attention dans les modeles de sequence a sequence pour la prediction des actes de dialogue—0
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