Multi-hop question answering (QA) is a challenging task requiring QA systems
to perform complex reasoning over multiple documents and provide supporting
facts together with the exact answer. Existing works tend to utilize
graph-based reasoning and question decomposition to obtain the reasoning chain,
which inevitably introduces additional complexity and cumulative error to the
system. To address the above issue, we propose a simple yet effective novel
framework, From Easy to Hard (FE2H), to remove distracting information and
obtain better contextual representations for the multi-hop QA task. Inspired by
the iterative document selection process and the progressive learning custom of
humans, FE2H divides both the document selector and reader into two stages
following an easy-to-hard manner. Specifically, we first select the document
most relevant to the question and then utilize the question together with this
document to select other pertinent documents. As for the QA phase, our reader
is first trained on a single-hop QA dataset and then transferred into the
multi-hop QA task. We comprehensively evaluate our model on the popular
multi-hop QA benchmark HotpotQA. Experimental results demonstrate that our
method ourperforms all other methods in the leaderboard of HotpotQA (distractor
setting).

本研究提出了一种名为 FE2H 的简单而有效的多跳问答（QA）框架，其采用迭代式文档选择和渐进式学习策略，分阶段进行文档选择和阅读，并在单跳 QA 数据集中训练 QA 读取器，成功应用于 HotpotQA 数据集，并在百分之六十七点一的准确率上超过现有的其他 QA 方法。