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

3 Jun 2014Kyunghyun Cho • Bart van Merrienboer • Caglar Gulcehre • Dzmitry Bahdanau • Fethi Bougares • Holger Schwenk • Yoshua Bengio

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.

Full paper

Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Machine Translation WMT2014 English-French CSLM + RNN + WP BLEU score 34.54 # 23