  We propose EAR, a query Expansion And Reranking approach for improving
passage retrieval, with the application to open-domain question answering. EAR
first applies a query expansion model to generate a diverse set of queries, and
then uses a query reranker to select the ones that could lead to better
retrieval results. Motivated by the observation that the best query expansion
often is not picked by greedy decoding, EAR trains its reranker to predict the
rank orders of the gold passages when issuing the expanded queries to a given
retriever. By connecting better the query expansion model and retriever, EAR
significantly enhances a traditional sparse retrieval method, BM25.
Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points in in-domain
and out-of-domain settings, respectively, when compared to a vanilla query
expansion model, GAR, and a dense retrieval model, DPR.
