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Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have beenintroduced recently as an efficient and effective way to perform retrieval withinverted indexes. They aim to learn term importance and, in some cases,document expansions, to provide a more effective document ranking compared totraditional bag-of-words retrieval models such as BM25. However, these sparseneural retrievers have been shown to increase the computational costs andlatency of query processing compared to their classical counterparts. Tomitigate this, we apply a well-known family of techniques for boosting theefficiency of query processing over inverted indexes: static pruning. Weexperiment with three static pruning strategies, namely document-centric,term-centric and agnostic pruning, and we assess, over diverse datasets, thatthese techniques still work with sparse neural retrievers. In particular,static pruning achieves $2\times$ speedup with negligible effectiveness loss($\leq 2\%$ drop) and, depending on the use case, even $4\times$ speedup withminimal impact on the effectiveness ($\leq 8\%$ drop). Moreover, we show thatneural rerankers are robust to candidates from statically pruned indexes.

A Static Pruning Study on Sparse Neural Retrievers