Dense Passage Retrieval for Open-Domain Question Answering

• URL: http://arxiv.org/abs/2004.04906v3
• Date: Wed, 30 Sep 2020 21:27:13 GMT
• Title: Dense Passage Retrieval for Open-Domain Question Answering
• Authors: Vladimir Karpukhin, Barlas O\u{g}uz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
• Abstract summary: We show that retrieval can be practically implemented using dense representations alone. Our dense retriever outperforms a strong Lucene-BM25 system largely by 9%-19% absolute in terms of top-20 passage retrieval accuracy.
• Score: 49.028342823838486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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