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：本文介绍了PASH在TREC 2021深度学习赛道中的参与工作。在召回阶段，我们采用了稀疏检索与稠密检索相结合的策略。在多阶段排序环节，基于通用知识与文档级数据持续预训练的模型，我们依次使用了逐点排序与配对排序策略。相较于TREC 2020深度学习赛道，我们额外引入了生成模型T5以进一步提升性能。