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

Understand

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.