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.

翻译：本文描述了PASH在TREC 2021深层学习轨迹中的参与情况。 在回顾阶段,我们采取了一种将稀少和密集的检索方法相结合的计划。在多阶段排名阶段,根据在一般知识和文件数据方面不断接受预先培训的模型,采用一对一的点和两对一的排名战略。与TREC 2020深层学习轨迹相比,我们还引入了基因模型T5,以进一步提高业绩。