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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 181–190 of 1773 papers

TitleStatusHype
Towards Accurate Translation via Semantically Appropriate Application of Lexical ConstraintsCode0
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models—0
Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili—0
Learning Multilingual Sentence Representations with Cross-lingual Consistency RegularizationCode0
Rethinking Translation Memory Augmented Neural Machine Translation—0
Improving Long Context Document-Level Machine Translation—0
On Search Strategies for Document-Level Neural Machine Translation—0
MobileNMT: Enabling Translation in 15MB and 30msCode1
Extract and Attend: Improving Entity Translation in Neural Machine Translation—0
Assessing the Importance of Frequency versus Compositionality for Subword-based Tokenization in NMTCode0
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