Multi-hop question answering (QA) requires an information retrieval (IR)
system that can find \emph{multiple} supporting evidence needed to answer the
question, making the retrieval process very challenging. This paper introduces
an IR technique that uses information of entities present in the initially
retrieved evidence to learn to `\emph{hop}' to other relevant evidence. In a
setting, with more than \textbf{5 million} Wikipedia paragraphs, our approach
leads to significant boost in retrieval performance. The retrieved evidence
also increased the performance of an existing QA model (without any training)
on the \hotpot benchmark by \textbf{10.59} F1.

本文介绍了一种信息检索技术，利用最初检索到的证据中存在的实体信息来学习到其他相关证据，并在超过 500 万个维基百科段落的语境下，取得了显着的检索性能提升。此外，检索到的证据还使得现有的 QA 模型（无需任何训练）在 Hotpot 基准测试中 F1 指标提升了 10.59 个点。