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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 201–210 of 1773 papers

TitleStatusHype
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine TranslationCode0
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine TranslationCode0
Finding Memo: Extractive Memorization in Constrained Sequence Generation TasksCode0
Exploring Unsupervised Pretraining Objectives for Machine TranslationCode0
Beyond BLEU: Training Neural Machine Translation with Semantic SimilarityCode0
Faithful Target Attribute Prediction in Neural Machine TranslationCode0
An Empirical Study of Consistency Regularization for End-to-End Speech-to-Text TranslationCode0
First the worst: Finding better gender translations during beam searchCode0
F-MALLOC: Feed-forward Memory Allocation for Continual Learning in Neural Machine TranslationCode0
Better Neural Machine Translation by Extracting Linguistic Information from BERTCode0
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