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

HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions