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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 311–320 of 1773 papers

TitleStatusHype
Coreference and Coherence in Neural Machine Translation: A Study Using Oracle Experiments—0
A Two-Stage Curriculum Training Framework for NMT—0
A Comparison of Sentence-Weighting Techniques for NMT—0
Analyzing Neural MT Search and Model Performance—0
Attention Weights in Transformer NMT Fail Aligning Words Between Sequences but Largely Explain Model Predictions—0
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation—0
Controlling Utterance Length in NMT-based Word Segmentation with Attention—0
Corpora for Document-Level Neural Machine Translation—0
Attention Link: An Efficient Attention-Based Low Resource Machine Translation Architecture—0
Analysing concatenation approaches to document-level NMT in two different domains—0
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