Query on Attention Architecture

Question

As we most know that, Attention is focuses on specific parts of the input sequence those are most relevant in generating output sequence.

Ex: The driver could not drive the car fast because it had a problem.

1. how attention finds the specific parts(here it is 'it') in input and how it will assign score for the token?

2. is attention context- based model?

3. how to obtain attention maps (query, key, value)?

4. On what basis attention assigns higher weights to input tokens?

Answer 1

1. Neural networks are considered black boxes because they are not interpretable: we don't know why they compute the results they do.

2. Yes.

3. To get attention maps you can you the BERTViz library, e.g.:

   

4. Same answer as 1).