  Neural information retrieval architectures based on transformers such as BERT
are able to significantly improve system effectiveness over traditional sparse
models such as BM25. Though highly effective, these neural approaches are very
expensive to run, making them difficult to deploy under strict latency
constraints. To address this limitation, recent studies have proposed new
families of learned sparse models that try to match the effectiveness of
learned dense models, while leveraging the traditional inverted index data
structure for efficiency. Current learned sparse models learn the weights of
terms in documents and, sometimes, queries; however, they exploit different
vocabulary structures, document expansion techniques, and query expansion
strategies, which can make them slower than traditional sparse models such as
BM25. In this work, we propose a novel indexing and query processing technique
that exploits a traditional sparse model's "guidance" to efficiently traverse
the index, allowing the more effective learned model to execute fewer scoring
operations. Our experiments show that our guided processing heuristic is able
to boost the efficiency of the underlying learned sparse model by a factor of
four without any measurable loss of effectiveness.
