  Interpretable multi-hop reading comprehension (RC) over multiple documents is
a challenging problem because it demands reasoning over multiple information
sources and explaining the answer prediction by providing supporting evidences.
In this paper, we propose an effective and interpretable Select, Answer and
Explain (SAE) system to solve the multi-document RC problem. Our system first
filters out answer-unrelated documents and thus reduce the amount of
distraction information. This is achieved by a document classifier trained with
a novel pairwise learning-to-rank loss. The selected answer-related documents
are then input to a model to jointly predict the answer and supporting
sentences. The model is optimized with a multi-task learning objective on both
token level for answer prediction and sentence level for supporting sentences
prediction, together with an attention-based interaction between these two
tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed
SAE system achieves top competitive performance in distractor setting compared
to other existing systems on the leaderboard.
