  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.
