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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 1451–1500 of 1773 papers

TitleStatusHype
Correcting Length Bias in Neural Machine Translation—0
Revisiting Character-Based Neural Machine Translation with Capacity and Compression—0
An Operation Sequence Model for Explainable Neural Machine TranslationCode0
A Tree-based Decoder for Neural Machine TranslationCode0
A Study of Reinforcement Learning for Neural Machine TranslationCode0
Contextual Parameter Generation for Universal Neural Machine TranslationCode0
Meta-Learning for Low-Resource Neural Machine Translation—0
Exploring Recombination for Efficient Decoding of Neural Machine TranslationCode0
Paraphrases as Foreign Languages in Multilingual Neural Machine Translation—0
Style Transfer as Unsupervised Machine Translation—0
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation—0
Learning When to Concentrate or Divert Attention: Self-Adaptive Attention Temperature for Neural Machine TranslationCode0
Training Deeper Neural Machine Translation Models with Transparent AttentionCode0
Measuring Semantic Abstraction of Multilingual NMT with Paraphrase Recognition and Generation Tasks—0
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing—0
Neural Machine Translation of Text from Non-Native SpeakersCode0
Regularizing Neural Machine Translation by Target-bidirectional Agreement—0
D-PAGE: Diverse Paraphrase Generation—0
Ancient-Modern Chinese Translation with a Large Training Dataset—0
Debugging Neural Machine TranslationsCode0
code2seq: Generating Sequences from Structured Representations of CodeCode0
Incorporating Syntactic Uncertainty in Neural Machine Translation with a Forest-to-Sequence Model—0
Neural Machine Translation Incorporating Named Entity—0
KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning—0
Low-Latency Neural Speech Translation—0
Adaptive Weighting for Neural Machine TranslationCode0
Neural Machine Translation with Decoding History Enhanced Attention—0
Improving Neural Machine Translation by Incorporating Hierarchical Subword Features—0
Tailoring Neural Architectures for Translating from Morphologically Rich Languages—0
Effective Parallel Corpus Mining using Bilingual Sentence Embeddings—0
Training Neural Machine Translation using Word Embedding-based Loss—0
NMT-based Cross-lingual Document Embeddings—0
Finding Better Subword Segmentation for Neural Machine TranslationCode0
Otem&Utem: Over- and Under-Translation Evaluation Metric for NMTCode0
Recurrent Stacking of Layers for Compact Neural Machine Translation Models—0
NMT-Keras: a Very Flexible Toolkit with a Focus on Interactive NMT and Online Learning—0
Testing Untestable Neural Machine Translation: An Industrial Case—0
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine TranslationCode0
Moon IME: Neural-based Chinese Pinyin Aided Input Method with Customizable Association—0
Learning Distributional Token Representations from Visual Features—0
Named-Entity Tagging and Domain adaptation for Better Customized Translation—0
How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures—0
Neural Hidden Markov Model for Machine Translation—0
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags—0
Forest-Based Neural Machine Translation—0
Neural Machine Translation Techniques for Named Entity TransliterationCode0
NICT Self-Training Approach to Neural Machine Translation at NMT-2018—0
Unsupervised Source Hierarchies for Low-Resource Neural Machine Translation—0
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation—0
Are BLEU and Meaning Representation in Opposition?—0
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