Hybrid and Collaborative Passage Reranking

Zongmeng Zhang, Wengang Zhou, Jiaxin Shi, Houqiang Li

Abstract: In information retrieval system, the initial passage retrieval results may be unsatisfactory, which can be refined by a reranking scheme. Existing solutions to passage reranking focus on enriching the interaction between query and each passage separately, neglecting the context among the top-ranked passages in the initial retrieval list. To tackle this problem, we propose a Hybrid and Collaborative Passage Reranking (HybRank) method, which leverages the substantial similarity measurements of upstream retrievers for passage collaboration and incorporates the lexical and semantic properties of sparse and dense retrievers for reranking. Besides, built on off-the-shelf retriever features, the flexible plug-in HybRank is capable of enhancing arbitrary passage list. Extensive experiments demonstrate the stable improvements of performance over prevalent retrieval methods, and verify the effectiveness of the core components in HybRank.

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