  In this study, we propose a novel graph neural network called
propagate-selector (PS), which propagates information over sentences to
understand information that cannot be inferred when considering sentences in
isolation. First, we design a graph structure in which each node represents an
individual sentence, and some pairs of nodes are selectively connected based on
the text structure. Then, we develop an iterative attentive aggregation and a
skip-combine method in which a node interacts with its neighborhood nodes to
accumulate the necessary information. To evaluate the performance of the
proposed approaches, we conduct experiments with the standard HotpotQA dataset.
The empirical results demonstrate the superiority of our proposed approach,
which obtains the best performances, compared to the widely used
answer-selection models that do not consider the intersentential relationship.
