Hybrid Retrieval and Multi-stage Text Ranking Solution at TREC 2022 Deep
  Learning Track
Guangwei Xu Yangzhao Zhang Longhui Zhang Dingkun Long Pengjun Xie Ruijie Guo
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 performance. Evaluation
results demonstrate the effectiveness of our proposed approach. Our models
achieve the 1st and 4th rank on the test set of passage ranking and document
ranking respectively.