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arXiv · 2004.04906

Dense Passage Retrieval for Open-Domain Question Answering

Vladimir Karpukhin·Barlas Oğuz·Sewon Min·Patrick Lewis·Ledell Wu·Sergey Edunov·Danqi Chen·Wen-tau Yih
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Abstract

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.

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Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih. 2020-04-10. Dense Passage Retrieval for Open-Domain Question Answering. https://arxiv.org/abs/2004.04906

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