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

PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer