Synonym Substitution Effects on Recall@k in Hybrid Sparse-Dense Retrievers Versus Pure Dense Models on TREC Deep Learning

Abstract

Abstract: Large-scale text retrieval technology has been widely used in various practical business scenarios. This paper presents our systems for the TREC 2022 Deep Learning Track. We explain the hybrid text retrieval and multi-stage text ranking method adopted in our solution. The retrieval stage combined the two structures of traditional sparse retrieval and neural dense retrieval. In the ranking stage, in addition to the full interaction-based ranking model built on large pre-trained language model, we also proposes a lightweight sub-ranking module to further enhance the final text ranking performanc

Research Question

How does synonym substitution impact the recall@k metrics of hybrid sparse-dense retrieval systems on TREC Deep Learning datasets compared to pure dense retrievers?

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