  Open-domain question answering relies on efficient passage retrieval to
select candidate contexts, where traditional sparse vector space models, such
as TF-IDF or BM25, are the de facto method. In this work, we show that
retrieval can be practically implemented using dense representations alone,
where embeddings are learned from a small number of questions and passages by a
simple dual-encoder framework. When evaluated on a wide range of open-domain QA
datasets, our dense retriever outperforms a strong Lucene-BM25 system largely
by 9%-19% absolute in terms of top-20 passage retrieval accuracy, and helps our
end-to-end QA system establish new state-of-the-art on multiple open-domain QA
benchmarks.
