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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 1651–1700 of 1773 papers

TitleStatusHype
Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary—0
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation—0
Sequence-to-Dependency Neural Machine Translation—0
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation—0
Visualizing and Understanding Neural Machine Translation—0
Chunk-based Decoder for Neural Machine Translation—0
Stronger Baselines for Trustable Results in Neural Machine TranslationCode0
Memory-augmented Chinese-Uyghur Neural Machine Translation—0
English-Japanese Neural Machine Translation with Encoder-Decoder-ReconstructorCode0
Encoder-Decoder Shift-Reduce Syntactic ParsingCode0
THUMT: An Open Source Toolkit for Neural Machine TranslationCode0
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation—0
Towards Neural Phrase-based Machine TranslationCode0
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality EstimationCode0
Attention-based Vocabulary Selection for NMT Decoding—0
Search Engine Guided Non-Parametric Neural Machine Translation—0
Sharp Models on Dull Hardware: Fast and Accurate Neural Machine Translation Decoding on the CPUCode0
Chunk-Based Bi-Scale Decoder for Neural Machine TranslationCode0
Modeling Source Syntax for Neural Machine Translation—0
A Teacher-Student Framework for Zero-Resource Neural Machine Translation—0
Deep Neural Machine Translation with Linear Associative Unit—0
A GRU-Gated Attention Model for Neural Machine Translation—0
Neural System Combination for Machine Translation—0
Adversarial Neural Machine Translation—0
An Empirical Analysis of NMT-Derived Interlingual Embeddings and their Use in Parallel Sentence Identification—0
Sparse Communication for Distributed Gradient DescentCode1
Towards String-to-Tree Neural Machine Translation—0
Translation of Patent Sentences with a Large Vocabulary of Technical Terms Using Neural Machine Translation—0
Exploiting Cross-Sentence Context for Neural Machine TranslationCode0
Neural Machine Translation Model with a Large Vocabulary Selected by Branching Entropy—0
Unfolding and Shrinking Neural Machine Translation Ensembles—0
Building a Neural Machine Translation System Using Only Synthetic Parallel Data—0
Practical Neural Machine Translation—0
Human Evaluation of Multi-modal Neural Machine Translation: A Case-Study on E-Commerce Listing Titles—0
Using Images to Improve Machine-Translating E-Commerce Product Listings.—0
Neural Automatic Post-Editing Using Prior Alignment and Reranking—0
Neural vs. Phrase-Based Machine Translation in a Multi-Domain Scenario—0
Improving Neural Machine Translation with Conditional Sequence Generative Adversarial NetsCode0
Massive Exploration of Neural Machine Translation ArchitecturesCode0
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages—0
Learning to Parse and Translate Improves Neural Machine TranslationCode0
Neural Machine Translation with Source-Side Latent Graph Parsing—0
Beam Search Strategies for Neural Machine Translation—0
Ensemble Distillation for Neural Machine Translation—0
Multilingual Multi-modal Embeddings for Natural Language Processing—0
Predicting Target Language CCG Supertags Improves Neural Machine Translation—0
Incorporating Global Visual Features into Attention-Based Neural Machine Translation—0
QCRI Machine Translation Systems for IWSLT 16—0
An Empirical Comparison of Simple Domain Adaptation Methods for Neural Machine Translation—0
Towards Decoding as Continuous Optimization in Neural Machine Translation—0
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