Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

E260052

natural language processing paper research paper scientific article

"Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation" is a seminal research paper that introduced the RNN encoder–decoder architecture to learn continuous phrase representations for improving statistical machine translation quality.

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All labels observed (2)

Label	Occurrences
Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation canonical	2
RNN encoder–decoder	1

Statements (47)

Predicate	Object
instanceOf	natural language processing paper ⓘ research paper ⓘ scientific article ⓘ
approach	learning continuous-space phrase representations ⓘ using neural networks to score phrase pairs in phrase-based SMT ⓘ
author	Bart van Merriënboer ⓘ Caglar Gulcehre ⓘ Dzmitry Bahdanau ⓘ Fethi Bougares ⓘ Holger Schwenk ⓘ Kyunghyun Cho ⓘ Yoshua Bengio ⓘ
citationImpact	highly cited ⓘ
codeAvailability	reference implementations were later released by the community ⓘ
evaluation	improvement of BLEU scores in phrase-based SMT ⓘ
field	deep learning ⓘ machine learning ⓘ machine translation ⓘ natural language processing ⓘ
firstAuthor	Kyunghyun Cho ⓘ
influenced	attention-based neural machine translation ⓘ neural machine translation ⓘ sequence-to-sequence learning ⓘ
inputType	source language phrase ⓘ
introducedConcept	gated recurrent unit ⓘ
languagePair	English–French ⓘ
learningParadigm	supervised learning ⓘ
mainContribution	demonstrated that learned phrase representations improve statistical machine translation quality ⓘ introduced a gated recurrent unit (GRU) as a new recurrent neural network unit ⓘ introduced an RNN encoder–decoder architecture to learn continuous phrase representations ⓘ
outputType	target language phrase ⓘ
preNeuralMTContext	designed to augment phrase-based statistical machine translation systems ⓘ
proposedArchitecture	Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation self-linksurface differs ⓘ surface form: RNN encoder–decoder recurrent neural network encoder–decoder ⓘ
publicationType	conference paper ⓘ
publishedIn	EMNLP ⓘ surface form: EMNLP 2014
publisher	Association for Computational Linguistics ⓘ
relatedTo	Neural Machine Translation by Jointly Learning to Align and Translate ⓘ Sequence to Sequence Learning with Neural Networks ⓘ
shortTitle	RNN Encoder–Decoder for Statistical Machine Translation ⓘ
status	seminal work in neural machine translation ⓘ
task	statistical machine translation ⓘ
title	Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation self-link ⓘ
usesModel	neural language model ⓘ recurrent neural network ⓘ
venue	EMNLP ⓘ surface form: Conference on Empirical Methods in Natural Language Processing
year	2014 ⓘ

Referenced by (3)

Full triples — surface form annotated when it differs from this entity's canonical label.

Quoc V. Le → coAuthorOf → Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation ⓘ

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation → title → Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation self-link ⓘ

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation → proposedArchitecture → Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation self-linksurface differs ⓘ

this entity surface form: RNN encoder–decoder