STAT946F17/ Dance Dance Convolution

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: Encoder-decoder architecture – example of a general approach for NMT.

(Figure 1)^[1]

References

1. https://github.com/tensorflow/nmt