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.

本文提出了一个可解释的 Select, Answer and Explain (SAE) 系统解决多文档阅读理解问题，该系统通过使用学习排名损失训练文档分类器来选择与答案有关的文档，并使用多任务学习目标和基于注意的交互来优化预测答案和支持句子的模型，在 HotpotQA 数据集上取得了顶尖性能。