  Neural retrieval models (NRMs) have been shown to outperform their
statistical counterparts owing to their ability to capture semantic meaning via
dense document representations. These models, however, suffer from poor
interpretability as they do not rely on explicit term matching. As a form of
local per-query explanations, we introduce the notion of equivalent queries
that are generated by maximizing the similarity between the NRM's results and
the result set of a sparse retrieval system with the equivalent query. We then
compare this approach with existing methods such as RM3-based query expansion
and contrast differences in retrieval effectiveness and in the terms generated
by each approach.
