17 citations · 26 across the 3 of their papers we have counts for
3 papers
cs.IR2022
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Hang Li, Ahmed Mourad, Bevan Koopman +1
Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation…
cs.IR2022★ 17 cited
To Interpolate or not to Interpolate: PRF, Dense and Sparse Retrievers
Hang Li, Shuai Wang, Shengyao Zhuang +4
Current pre-trained language model approaches to information retrieval can be broadly divided into two categories: sparse retrievers (to which belong also non-neural approaches suc…
cs.IR2022★ 9 cited
Implicit Feedback for Dense Passage Retrieval: A Counterfactual Approach
Shengyao Zhuang, Hang Li, Guido Zuccon
In this paper we study how to effectively exploit implicit feedback in Dense Retrievers (DRs). We consider the specific case in which click data from a historic click log is availa…