Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefit from the extra context that PRF provides. It presents PGT, a graph-based Transformer that sparsifies attention between graph nodes to enable PRF while avoiding the high computational complexity of most Transformer architectures. Experiments show that PGT improves upon non-PRF Transformer reranker, and it is at least as accurate as Transformer PRF models that use full attention, but with lower computational costs.

中文翻译：

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PGT：使用基于图的变压器的伪相关反馈

伪相关反馈（PRF）的大多数研究都是在向量空间和概率检索模型中完成的。本文表明，基于Transformer的重编程序也可以从PRF提供的额外上下文中受益。它介绍了PGT，这是一种基于图的Transformer，它可以减轻图节点之间的注意力以启用PRF，同时避免大多数Transformer体系结构的高计算复杂性。实验表明，PGT在非PRF Transformer重新排序器的基础上有所改进，它的准确性至少与全神贯注的Transformer PRF模型一样，但计算成本较低。