  Recent developments in representational learning for information retrieval
can be organized in a conceptual framework that establishes two pairs of
contrasts: sparse vs. dense representations and unsupervised vs. learned
representations. Sparse learned representations can further be decomposed into
expansion and term weighting components. This framework allows us to understand
the relationship between recently proposed techniques such as DPR, ANCE,
DeepCT, DeepImpact, and COIL, and furthermore, gaps revealed by our analysis
point to "low hanging fruit" in terms of techniques that have yet to be
explored. We present a novel technique dubbed "uniCOIL", a simple extension of
COIL that achieves to our knowledge the current state-of-the-art in sparse
retrieval on the popular MS MARCO passage ranking dataset. Our implementation
using the Anserini IR toolkit is built on the Lucene search library and thus
fully compatible with standard inverted indexes.
