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PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
May 18, 2022 Β· Declared Dead Β· π Text Retrieval Conference
"No code URL or promise found in abstract"
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Authors
Yixuan Qiao, Shanshan Zhao, Jun Wang, Hao Chen, Tuozhen Liu, Xianbin Ye, Xin Tang, Rui Fang, Peng Gao, Wenfeng Xie, Guotong Xie
arXiv ID
2205.11245
Category
cs.IR: Information Retrieval
Cross-listed
cs.CL
Citations
1
Venue
Text Retrieval Conference
Last Checked
4 months ago
Abstract
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.
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