From the Amazing Blog - FloydHub Blog- Attention Mechanisms
Attention takes two sentences, turns them into a matrix where the words of one sentence form the columns, and the words of another sentence form the rows, and then it makes matches, identifying relevant context. This is very useful in machine translation.
When we think about the English word “Attention”, we know that it means directing your focus at something and taking greater notice. The Attention mechanism in Deep Learning is based off this concept of directing your focus, and it pays greater attention to certain factors when processing the data.
In broad terms, Attention is one component of a network’s architecture, and is in charge of managing and quantifying the interdependence:
- Between the input and output elements (General Attention)
- Within the input elements (Self-Attention)
Let me give you an example of how Attention works in a translation task. Say we have the sentence “How was your day”, which we would like to translate to the French version - “Comment se passe ta journée”. What the Attention component of the network will do for each word in the output sentence is map the important and relevant words from the input sentence and assign higher weights to these words, enhancing the accuracy of the output prediction.
Weights are assigned to input words at each step of the translation
I recommend having a read through this article - Attention Mechanism
More at - Attention Mechanisms and Memory Networks