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Hybrid and Collaborative Passage Reranking

arXiv 2305.09313 ↗First posted 16 May 2023cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

In passage 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, HybRank is a plug-in reranker capable of enhancing arbitrary passage lists including previously reranked ones. Extensive experiments demonstrate the stable improvements of performance over prevalent retrieval and reranking methods, and verify the effectiveness of the core components of HybRank.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 39 of its 58 references that are in the map. First posted on arXiv 16 May 2023.

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References · 39

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27 more
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