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PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer

2021-01-20 01:07:47
HongChien Yu, Zhuyun Dai, Jamie Callan
arXiv_AI
     
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2101.07918

PDF

https://arxiv.org/pdf/2101.07918.pdf

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