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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 1476–1500 of 1773 papers

TitleStatusHype
Testing Untestable Neural Machine Translation: An Industrial Case—0
Named-Entity Tagging and Domain adaptation for Better Customized Translation—0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
A Deep Learning Based Approach to Transliteration—0
OpenNMT System Description for WNMT 2018: 800 words/sec on a single-core CPU—0
NICT Self-Training Approach to Neural Machine Translation at NMT-2018—0
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine TranslationCode0
Learning Distributional Token Representations from Visual Features—0
Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation—0
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association—0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures—0
Forest-Based Neural Machine Translation—0
Are BLEU and Meaning Representation in Opposition?—0
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags—0
Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation—0
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation—0
Neural Hidden Markov Model for Machine Translation—0
Neural Machine Translation with Key-Value Memory-Augmented Attention—0
Neural Machine Translation for Low Resource Languages using Bilingual Lexicon Induced from Comparable Corpora—0
On Adversarial Examples for Character-Level Neural Machine TranslationCode0
Learning from Chunk-based Feedback in Neural Machine Translation—0
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation—0
Morphological and Language-Agnostic Word Segmentation for NMTCode0
An Evaluation of Neural Machine Translation Models on Historical Spelling NormalizationCode0
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model—0
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