This paper describes the PASH participation in TREC 2021 Deep Learning Track. In the recall stage, we adopt a scheme combining sparse and dense retrieval method. In the multi-stage ranking phase, point-wise and pair-wise ranking strategies are used one after another based on model continual pre-trained on general knowledge and document-level data. Compared to TREC 2020 Deep Learning Track, we have additionally introduced the generative model T5 to further enhance the performance.
Cite
@article{arxiv.2205.11245,
title = {PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking},
author = {Yixuan Qiao and Shanshan Zhao and Jun Wang and Hao Chen and Tuozhen Liu and Xianbin Ye and Xin Tang and Rui Fang and Peng Gao and Wenfeng Xie and Guotong Xie},
journal= {arXiv preprint arXiv:2205.11245},
year = {2026}
}