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PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer

HongChien Yu
Computer Science Department
School of Computer Science
Carnegie Mellon University
hongqiay@cs.cmu.edu
Zhuyun Dai
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
zhuyund@cs.cmu.edu
Jamie Callan
Language Technologies Institute
School of Computer Science
Carnegie Mellon University
callan@cs.cmu.edu

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.

Source Code

Source code are in the GihHub repositorty. It covers:

Citation

H. Yu, Z. Dai and J. Callan. PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer In Proceedings of the 43rd European Conference On Information Retrieval (ECIR) 2021.
Updated on Jan 8, 2021.
HongChien Yu