We tackle this issue with two strategies: 1) we use a novel importance module (explained later) to focus on important answer aspects, and 2) we only consider those context nodes that have overlap with the question.
for each context node (i.e., a sequence of words) of a candidate, we first compute the longest common subsequence between it and the question, we then encode it via a BiLSTM only if we get a non-stopwords substring.