Given a hypothesis statement H qa representing a candidate answer, and the set of premise sentences {P i }, our proposed architecture uses the same function f e for two components: (a) a sentence relevance module that scores each P i based on its potential relevance to H qa , with the goal of weeding out distractors; and (b) a relevance-weighted aggregator that combines entailment information from multiple P i .
we build effective entailment aware representations of larger contexts (i.e., multiple sentences) from those of small contexts (i.e., individual sentences).