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

@article{Cho2014LearningPR,
  title={Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation},
  author={Kyunghyun Cho and Bart van Merrienboer and Çaglar G{\"u}lçehre and Fethi Bougares and Holger Schwenk and Yoshua Bengio},
  journal={ArXiv},
  year={2014},
  volume={abs/1406.1078}
}
In this paper, we propose a novel neural network model called RNN Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixed-length vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation… CONTINUE READING

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