Summary

In this chapter, we talked in detail about NMT systems. Machine translation is the task of translating a given text corpus from a source language to a target language. First we talked about the history of machine translation briefly to build a sense of appreciation for what has gone into machine translation, to become what it is today. We saw that today the highest performing machine translation systems are actually NMT systems. Next we talked about the fundamental concept of these systems and decomposed the model into the embedding layer, the encoder, the context vector, and the decoder. We first established the benefit of having an embedding layer as it gives semantic representations of words compared to one-hot-encoded vectors. Then we understood the objective of the encoder, which is to learn a good fixed dimensional vector that represents the source sentence. Next, once the fixed dimensional context vector was learned, we used this to initialize the decoder. The decoder is responsible...