Learning Phrase Representations using RNN Encoder- …

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1724 1734,October 25-29, 2014, Doha, 2014 Association for Computational LinguisticsLearning Phrase Representations using RNN Encoder Decoderfor statistical Machine TranslationKyunghyun ChoBart van Merri enboer Caglar GulcehreUniversit e de Montr BahdanauJacobs University, Bougares Holger SchwenkUniversit e du Maine, BengioUniversit e de Montr eal, CIFAR Senior this paper, we propose a novel neu-ral network model called RNN Encoder Decoder that consists of two recurrentneural networks (RNN). One RNN en-codes a sequence of symbols into a fixed-length vector representation, and the otherdecodes the representation into another se-quence of symbols. The encoder and de-coder of the proposed model are jointlytrained to maximize the conditional prob-ability of a target sequence given a sourcesequence.

Learning Phrase Representations using RNN Encoder Decoder for Statistical Machine Translation Kyunghyun Cho Bart van Merri enboer Caglar Gulcehre¨ Universite de Montr´ eal´ firstname.lastname@umontreal.ca Dzmitry Bahdanau Jacobs University, Germany d.bahdanau@jacobs-university.de Fethi Bougares Holger Schwenk Universit´e du Maine, France

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