Query Term Expansion and Ranking Stability in TREC 2022 Deep Learning Track Systems

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 goal: What is the impact of query term expansion on the ranking stability of multi-stage text ranking systems in the TREC 2022 Deep Learning Track?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.