  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.
