  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 such as bag-of-words methods, e.g., BM25) and dense
retrievers. Each of these categories appears to capture different
characteristics of relevance. Previous work has investigated how relevance
signals from sparse retrievers could be combined with those from dense
retrievers via interpolation. Such interpolation would generally lead to higher
retrieval effectiveness. In this paper we consider the problem of combining the
relevance signals from sparse and dense retrievers in the context of Pseudo
Relevance Feedback (PRF). This context poses two key challenges: (1) When
should interpolation occur: before, after, or both before and after the PRF
process? (2) Which sparse representation should be considered: a zero-shot
bag-of-words model (BM25), or a learnt sparse representation? To answer these
questions we perform a thorough empirical evaluation considering an effective
and scalable neural PRF approach (Vector-PRF), three effective dense retrievers
(ANCE, TCTv2, DistillBERT), and one state-of-the-art learnt sparse retriever
(uniCOIL). The empirical findings from our experiments suggest that, regardless
of sparse representation and dense retriever, interpolation both before and
after PRF achieves the highest effectiveness across most datasets and metrics.
