What is recorded here?
Used one multilayer LSTM to encode a variable-length input sequence and a second LSTM to generate a variable-length output sequence, demonstrated on large-scale machine translation.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
This is the exact 2014 three-author encoder-decoder paper. Attention, Transformers and later production translation systems are separate contributions.
Read the original work
- Google Research: Sequence to Sequence Learning with Neural Networks
- Sutskever, Vinyals and Le, Sequence to Sequence Learning with Neural Networks
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Ilya Sutskever; Oriol Vinyals; Quoc V. Le
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.
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English catalogue narrative
Sutskever, Vinyals and Le: sequence-to-sequence learning
Used one multilayer LSTM to encode a variable-length input sequence and a second LSTM to generate a variable-length output sequence, demonstrated on large-scale machine translation.
Scientific scope
This is the exact 2014 three-author encoder-decoder paper. Attention, Transformers and later production translation systems are separate contributions.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Ilya Sutskever; Oriol Vinyals; Quoc V. Le
The named authors contributed to this scoped work; individual effort shares and later applications are not assigned by the byline.