With U and R, SCN constructs a word-word similarity matrix M 1 ∈ R n u ×n r and a sequence-sequence similarity matrix M 2 ∈ R n u ×n r as two input channels of a convolutional neural network (CNN).
the CNN then extracts important matching information from the two matrices and encodes the information into a matching vector v. ∀i, j, the (i, j)-th element of M 1 is defined by M 1 models the interaction between u and r on a word level.