Having gone through articles.

However, still not sure of the basic mechanism of attention. I see encoder, decoder, hidden state and diagrams, and the matrix out of blue.




As a first step, please confirm if below is a correct understanding.

If asked, "Related with crime, cocaine, police, smuggle but not with honest, legitimate. Who is this?". Drug-dealer can pop up. The correlations from drug-dealer to (crime, cocaine, police, smuggle, ...) can be quantified as a vector. Some people have built such vectors by going through wikipedia article sentences, BBC news articles, etc. A vector possibly would be a weight in DNN.

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When a NLP parser finds smuggle in a sentence, those correlation vectors help to find which words are more strongly correlated. Is this the basic mechanism of attention, which is, the correlation vectors in a nutshell?

For image to caption, an object detection extracts (frisbee, girl, hair, cloths, ...) and those vectors tell that girl is the word to pay attention to for frisbee.

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For translation, e.g. English to Spanish, I suppose there are two sets of vectors. One for English words and the other for Spanish, and association between a English word to a Spanish word is trained as a DNN model where training input is (english-vector, spanish-vector) and someone has prepared labels. Is this correct?

PS. A kind request not to list math formulas, or similar diagrams from the articles. The formulas should be the projections of solid intuitions that even a 6 grader can get. I appreciate such intuitions explained in layman's term as the answers.


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