Further, the word-by-context embedding for sequence t a is obtained by taking the weighted average over all word embeddings Intuitively, α i may be understood as the relevance score between the ith word in t a and sequence t b .
case study The proposed word-by-word alignment mechanism can be used to highlight the most informative words (and the corresponding matching features) wrt the relationship between vertices.