Corpus ID: 7961699

Sequence to Sequence Learning with Neural Networks

@inproceedings{Sutskever2014SequenceTS,
  title={Sequence to Sequence Learning with Neural Networks},
  author={Ilya Sutskever and Oriol Vinyals and Quoc V. Le},
  booktitle={NIPS},
  year={2014}
}
Deep Neural Networks (DNNs) are powerful models that have achieved excellent performance on difficult learning tasks. [...] Key Method Our method uses a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector. Our main result is that on an English to French translation task from the WMT-14 dataset, the translations produced by the LSTM achieve a BLEU score of 34.8 on the entire test set…Expand
A Hierarchical Neural Network for Sequence-to-Sequences Learning
Multi-task Sequence to Sequence Learning
Unsupervised Pretraining for Sequence to Sequence Learning
Von Mises-Fisher Loss for Training Sequence to Sequence Models with Continuous Outputs
Agreement on Target-Bidirectional LSTMs for Sequence-to-Sequence Learning
The Roles of Language Models and Hierarchical Models in Neural Sequence-to-Sequence Prediction
Data Generation Using Sequence-to-Sequence
Recurrent Neural Network-Based Semantic Variational Autoencoder for Sequence-to-Sequence Learning
Semi-supervised Sequence Learning
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