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.

翻译：在段落检索系统中，初始检索结果可能不尽人意，可通过重排序方案进行优化。现有段落重排序方法侧重于增强查询与每个段落间的单独交互，忽略了初始检索列表中排名靠前段落间的上下文关联。为解决该问题，我们提出一种混合与协同段落重排序方法（HybRank），该方法利用上游检索器的大规模相似度测量实现段落协同，并融合稀疏检索器与密集检索器的词汇与语义特性完成重排序。此外，基于现成检索器特征构建的HybRank作为一种插件式重排序器，能够增强任意段落列表（包括已重排序列表）。大量实验表明，本方法在主流检索与重排序方法上取得稳定性能提升，同时验证了HybRank核心组件的有效性。