Is there a natural way, in terms of structure of the layers of a NN, in order to pass 2 inputs vectors to the NN?
Example: text authorship identification
Input #1: sentence1 by unknown author encoded, as list of words from a dictionary
"The sky is blue" =>
x1 = [2, 23, 7, 76, 0, 0, 0, ..., 0](zero-padded to have a length of 1000 items)
Input #2: sentence2 by unknown author, idem
"The cat is sleeping" =>
x2 = [2, 65, 7, 121, 0, 0, 0, ..., 0]
y= a single number in [0, 1].
0 = different authors
1 = same author
0.9 = high probability of same author, etc.
Of course I could build the layers like this:
Input-size: None, 1000, 2 (x1, x2 stacked into a 1000x2 matrix) CNN ... ... Dense: None, 1
Then I could train with a dataset with 10,000 pairs of sentences of the same author (desired output: 1), and 10,000 pairs of sentences with different authors (desired output: 0).
But I don't know if it would work by just stacking x1 and x2 as a 1000x2 matrix.
TL;DR: What is the classical approach to build a NN taking 2 inputs and a single number as output which is a similarity index from 0% to 100%? (if possible with Sequential structure)