  We propose a design pattern for tackling text ranking problems, dubbed
"Expando-Mono-Duo", that has been empirically validated for a number of ad hoc
retrieval tasks in different domains. At the core, our design relies on
pretrained sequence-to-sequence models within a standard multi-stage ranking
architecture. "Expando" refers to the use of document expansion techniques to
enrich keyword representations of texts prior to inverted indexing. "Mono" and
"Duo" refer to components in a reranking pipeline based on a pointwise model
and a pairwise model that rerank initial candidates retrieved using keyword
search. We present experimental results from the MS MARCO passage and document
ranking tasks, the TREC 2020 Deep Learning Track, and the TREC-COVID challenge
that validate our design. In all these tasks, we achieve effectiveness that is
at or near the state of the art, in some cases using a zero-shot approach that
does not exploit any training data from the target task. To support
replicability, implementations of our design pattern are open-sourced in the
Pyserini IR toolkit and PyGaggle neural reranking library.
