论文标题
TREC的Pash 2021深度学习曲目：多阶段排名的生成增强模型
PASH at TREC 2021 Deep Learning Track: Generative Enhanced Model for Multi-stage Ranking
论文作者
论文摘要
本文描述了Pash参与TREC 2021深度学习曲目。在召回阶段，我们采用了一种结合稀疏和密集检索方法的方案。在多阶段排名阶段中，基于对通用知识和文档级数据的持续预先训练的模型，使用了点和成对排名策略。与TREC 2020深度学习轨道相比，我们还引入了生成模型T5，以进一步提高性能。
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.
