The factorization layer tries to decompose the alignment vector (constructed from the variations of a − b, a b and [a; b]), learning higher-order feature interactions between each compared alignment.
it models the second-order (pairwise) interactions between each feature in every alignment vector using factorized parameters, allowing more expressive comparison to be made over traditional fully-connected layers (FC).