  Learned sparse and dense representations capture different successful
approaches to text retrieval and the fusion of their results has proven to be
more effective and robust. Prior work combines dense and sparse retrievers by
fusing their model scores. As an alternative, this paper presents a simple
approach to densifying sparse representations for text retrieval that does not
involve any training. Our densified sparse representations (DSRs) are
interpretable and can be easily combined with dense representations for
end-to-end retrieval. We demonstrate that our approach can jointly learn sparse
and dense representations within a single model and then combine them for dense
retrieval. Experimental results suggest that combining our DSRs and dense
representations yields a balanced tradeoff between effectiveness and
efficiency.
