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

Venue

abs/1406.1078, vol. CoRR

Publication Year

2014

Keywords

Computer Science - Computation and Language,Computer Science - Machine Learning,Computer Science - Neural and Evolutionary Computing,Statistics - Machine Learning

Authors

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

Abstract

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 system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder-Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.