STAT946F17/ Dance Dance Convolution: Difference between revisions
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=Introduction= | =Introduction= | ||
Neural Machine Translation (NMT), which is based on deep neural networks and provides an end- to-end solution to machine translation, uses an RNN-based encoder-decoder architecture to model the entire translation process. Specifically, an NMT system first reads the source sentence using an encoder to build a "thought" vector, a sequence of numbers that represents the sentence meaning; a decoder, then, processes the "meaning" vector to emit a translation. [[File:VNFigure1.png|thumb|600px|center|Figure 1: Encoder-decoder architecture – example of a general approach for NMT.]] | Neural Machine Translation (NMT), which is based on deep neural networks and provides an end- to-end solution to machine translation, uses an RNN-based encoder-decoder architecture to model the entire translation process. Specifically, an NMT system first reads the source sentence using an encoder to build a "thought" vector, a sequence of numbers that represents the sentence meaning; a decoder, then, processes the "meaning" vector to emit a translation. (Figure 1)<sup>[[#References|[1]]]</sup> | ||
[[File:VNFigure1.png|thumb|600px|center|Figure 1: Encoder-decoder architecture – example of a general approach for NMT.]] | |||
=References= | =References= | ||
1. https://github.com/tensorflow/nmt | 1. https://github.com/tensorflow/nmt | ||
Revision as of 13:23, 24 November 2017
Introduction
Neural Machine Translation (NMT), which is based on deep neural networks and provides an end- to-end solution to machine translation, uses an RNN-based encoder-decoder architecture to model the entire translation process. Specifically, an NMT system first reads the source sentence using an encoder to build a "thought" vector, a sequence of numbers that represents the sentence meaning; a decoder, then, processes the "meaning" vector to emit a translation. (Figure 1)[1]
