Hybrid and Collaborative Passage Reranking
Zongmeng Zhang Wengang Zhou Jiaxin Shi Houqiang Li
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.