PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
Yixuan Qiao, Zhao, Shanshan, Jun Wang, Hao Chen, Tuozhen Liu, Xianbin Ye, Tang, Xin, Fang, Rui, Peng Gao, Xie, Wenfeng, Guotong Xie · arXiv (Cornell University) · 2022
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.