论文标题
HYRR：混合注入重新融合以进行通道检索
HYRR: Hybrid Infused Reranking for Passage Retrieval
论文作者
论文摘要
我们提出了用于通道检索（HYRR）的混合动力注入重新疗法，这是一个基于BM25和神经检索模型的混合动力的培训rerankers的框架。基于混合模型的检索器已显示出胜过BM25和单独的神经模型。我们的方法在训练重读者时利用了这种改善的性能，从而导致了强大的重读模型。 Reranker是一种跨意义的神经模型，被证明对不同的第一阶段检索系统具有鲁棒性，比仅在多阶段系统中对第一阶段检索器训练的Reranker实现了更好的性能。我们使用MARCO女士和使用Beir的零射击检索任务进行了有关监督段落检索任务的评估。经验结果在两种评估上都表现出强烈的表现。
We present Hybrid Infused Reranking for Passages Retrieval (HYRR), a framework for training rerankers based on a hybrid of BM25 and neural retrieval models. Retrievers based on hybrid models have been shown to outperform both BM25 and neural models alone. Our approach exploits this improved performance when training a reranker, leading to a robust reranking model. The reranker, a cross-attention neural model, is shown to be robust to different first-stage retrieval systems, achieving better performance than rerankers simply trained upon the first-stage retrievers in the multi-stage systems. We present evaluations on a supervised passage retrieval task using MS MARCO and zero-shot retrieval tasks using BEIR. The empirical results show strong performance on both evaluations.
