2020 DensePassageRetrievalforOpenDom

From GM-RKB
(Redirected from Karpukhin et al., 2020)
Jump to navigation Jump to search

Subject Headings: RAG Algorithm, Open-Domain QA.

Notes

Cited By

Quotes

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.

References

;

 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2020 DensePassageRetrievalforOpenDomWen-tau Yih
Danqi Chen
Ledell Wu
Sewon Min
Patrick Lewis
Vladimir Karpukhin
Sergey Edunov
Dense Passage Retrieval for Open-domain Question Answering10.48550/arXiv.2004.049062020