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Neural retrieval models (NRMs) have been shown to outperform theirstatistical counterparts owing to their ability to capture semantic meaning viadense document representations. These models, however, suffer from poorinterpretability as they do not rely on explicit term matching. As a form oflocal per-query explanations, we introduce the notion of equivalent queriesthat are generated by maximizing the similarity between the NRM's results andthe result set of a sparse retrieval system with the equivalent query. We thencompare this approach with existing methods such as RM3-based query expansionand contrast differences in retrieval effectiveness and in the terms generatedby each approach.

Explain like I am BM25: Interpreting a Dense Model's Ranked-List with a Sparse Approximation