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.

EAR 是一种用于改进段落检索的查询扩展和重新排序方法，适用于开放领域问答。 通过将更好的查询扩展模型与检索器更好地连接起来，EAR 显着增强了传统的稀疏检索方法 BM25。