HYRR: Hybrid Infused Reranking for Passage Retrieval

Understand

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.