Sequence to Sequence Learning with Neural Networks

E260048

machine learning paper neural networks paper scientific paper

"Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.

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All labels observed (1)

Label	Occurrences
Sequence to Sequence Learning with Neural Networks canonical	4

How this entity was disambiguated

This entity first appeared as the object of triple T2373680 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.

NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07

Target entity: Sequence to Sequence Learning with Neural Networks
Context triple: [Quoc V. Le, coAuthorOf, Sequence to Sequence Learning with Neural Networks]

A. WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
B. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
C. “A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
D. Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
E. Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
F. None of above. chosen
G. Unsure - the case is ambiguous/there is not enough information to decide.

NED2 Entity disambiguation (via description) gpt-5-mini-2025-08-07

Target entity: Sequence to Sequence Learning with Neural Networks
Target entity description: "Sequence to Sequence Learning with Neural Networks" is a seminal 2014 paper that introduced the sequence-to-sequence (seq2seq) neural network framework for tasks like machine translation, laying the groundwork for many modern NLP models.

A. WaveNet
WaveNet is a deep generative neural network architecture for raw audio that produces highly natural-sounding speech and other audio signals.
B. “Learning representations by back-propagating errors”
“Learning representations by back-propagating errors” is a landmark 1986 research paper that popularized the backpropagation algorithm for training multi-layer neural networks, helping to launch the modern field of deep learning.
C. “A fast learning algorithm for deep belief nets”
“A fast learning algorithm for deep belief nets” is a seminal 2006 paper by Geoffrey Hinton that introduced an efficient unsupervised pretraining method for deep neural networks using stacked restricted Boltzmann machines.
D. Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
E. Parallel WaveNet
Parallel WaveNet is a neural vocoder architecture that accelerates high-fidelity audio waveform generation by distilling the autoregressive WaveNet model into a fast, parallelizable form.
F. None of above. chosen

Statements (50)

Predicate	Object
instanceOf	machine learning paper ⓘ neural networks paper ⓘ scientific paper ⓘ
affiliationOfAuthors	Google ⓘ
approach	encoder-decoder architecture ⓘ recurrent neural networks ⓘ
author	Ilya Sutskever ⓘ Oriol Vinyals ⓘ Quoc V. Le ⓘ
citationStatus	highly cited ⓘ
conference	Neural Information Processing Systems 2014 ⓘ linked to: NeurIPS
demonstratedOn	English-to-French machine translation ⓘ WMT English-French dataset ⓘ
field	deep learning ⓘ machine learning ⓘ natural language processing ⓘ
firstAuthor	Ilya Sutskever ⓘ
impact	foundational work for modern neural sequence modeling ⓘ
influenceOn	Transformer-based models ⓘ attention-based sequence models ⓘ encoder-decoder architectures in deep learning ⓘ neural machine translation ⓘ sequence-to-sequence models in NLP ⓘ speech recognition sequence models ⓘ text summarization models ⓘ
keyIdea	map variable-length input sequences to variable-length output sequences ⓘ train encoder and decoder jointly to maximize conditional probability of target sequence ⓘ use a fixed-length vector representation of the input sequence ⓘ
mainContribution	application of encoder-decoder RNNs to sequence transduction tasks ⓘ demonstration of neural machine translation with RNN encoder-decoder ⓘ introduction of the sequence-to-sequence neural network framework ⓘ
organization	Google ⓘ
publicationYear	2014 ⓘ
publishedIn	Advances in Neural Information Processing Systems ⓘ linked to: NeurIPS NeurIPS 2014 ⓘ linked to: NeurIPS
publisher	Neural Information Processing Systems Foundation ⓘ linked to: NeurIPS
relatedConcept	encoder-decoder RNN ⓘ neural machine translation ⓘ sequence modeling ⓘ
result	achieved state-of-the-art performance on English-French translation at time of publication ⓘ
task	machine translation ⓘ sequence transduction ⓘ sequence-to-sequence learning ⓘ
technique	reversing the order of words in the source sentence ⓘ use of beam search for decoding ⓘ use of multi-layer LSTMs ⓘ
title	Sequence to Sequence Learning with Neural Networks ⓘ
usesModel	LSTM decoder ⓘ LSTM encoder ⓘ Long Short-Term Memory network ⓘ linked to: LSTM networks

How these facts were elicited

Referenced by (4)

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

Quoc V. Le → coAuthorOf → Sequence to Sequence Learning with Neural Networks ⓘ

Oriol Vinyals → notableWork → Sequence to Sequence Learning with Neural Networks ⓘ

Sequence to Sequence Learning with Neural Networks → title → Sequence to Sequence Learning with Neural Networks ⓘ

Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation → relatedTo → Sequence to Sequence Learning with Neural Networks ⓘ

All labels observed (1)

How this entity was disambiguated Show

Statements (50)

How these facts were elicited Show

Referenced by (4)

How this entity was disambiguated

How these facts were elicited