Paper: Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation

ACL ID D14-1179
Title Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2014
Authors

In this paper, we propose a novel neu- ral network model called RNN Encoder? Decoder that consists of two recurrent neural networks (RNN). One RNN en- codes a sequence of symbols into a fixed- length vector representation, and the other decodes the representation into another se- quence of symbols. The encoder and de- coder of the proposed model are jointly trained to maximize the conditional prob- ability of a target sequence given a source sequence. The performance of a statisti- cal machine translation system is empiri- cally found to improve by using the con- ditional probabilities of phrase pairs com- puted 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 meaning...