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)[1]
