Machine Translation
Back in the old days, traditional phrase-based translation systems performed their task by breaking up source sentences into multiple chunks and then translated them phrase-by-phrase. This led to disfluency in the translation outputs and was not quite like how we, humans, translate. We read the entire source sentence, understand its meaning, and then produce a translation. Neural Machine Translation (NMT)
In this notebook, a deep neural network that functions as part of an end-to-end machine translation . Model will accept English text as input and return the French translation of English text
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 sentence vector to emit a translation, as illustrated in Figure 1. This is often referred to as the encoder-decoder architecture. In this manner, NMT addresses the local translation problem in the traditional phrase-based approach: it can capture long-range dependencies in languages, e.g., gender agreements; syntax structures; etc., and produce much more fluent translations
NMT models vary in terms of their exact architectures. A natural choice for sequential data is the recurrent neural network (RNN), used by most NMT models. Usually an RNN is used for both the encoder and decoder.
The RNN models, however, differ in terms of:
(a) directionality – unidirectional or bidirectional;
(b) depth – single- or multi-layer;
(c) type – often either a vanilla RNN, a Long Short-term Memory (LSTM), or a gated recurrent unit (GRU)