Thus, matrix X b ∈ R d×Ma in (10) constitutes an attention-weighted embedding for X b .
the i-th column of X b , denoted as x b , can be understood as a weighted average over all the words in t b , where higher attention weights indicate better alignment (match) with the i-th word in t a .