  Collecting supporting evidence from large corpora of text (e.g., Wikipedia)
is of great challenge for open-domain Question Answering (QA). Especially, for
multi-hop open-domain QA, scattered evidence pieces are required to be gathered
together to support the answer extraction. In this paper, we propose a new
retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia
for complex question answering. Specifically, the hop in this paper is defined
as the combination of a hyperlink and the corresponding outbound link document.
The hyperlink is encoded as the mention embedding which models the structured
knowledge of how the outbound link entity is mentioned in the textual context,
and the corresponding outbound link document is encoded as the document
embedding representing the unstructured knowledge within it. Accordingly, we
build HopRetriever which retrieves hops over Wikipedia to answer complex
questions. Experiments on the HotpotQA dataset demonstrate that HopRetriever
outperforms previously published evidence retrieval methods by large margins.
Moreover, our approach also yields quantifiable interpretations of the evidence
collection process.
