HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions

TL;DR

HopRetriever introduces a novel method for retrieving multi-hop evidence from Wikipedia by combining hyperlink and document embeddings, significantly improving complex question answering performance.

Contribution

The paper proposes a new retrieval target called hop, integrating hyperlink and outbound link document embeddings for better evidence collection in open-domain QA.

Findings

Outperforms previous evidence retrieval methods on HotpotQA

Provides interpretable evidence collection process

Enhances multi-hop reasoning capabilities

Abstract

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.