PublishedCurated

Dense Passage Retrieval for Open-Domain QA

Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)10 April 2020

Abstract

Replaces term matching with a dual-encoder that embeds questions and passages into the same vector space. Relevance becomes a nearest-neighbour lookup, which lets retrieval find passages that share no vocabulary with the question.

arXivCode

BibTeX

@inproceedings{karpukhin2020dense,
  author    = {Vladimir Karpukhin and Barlas Oguz and Sewon Min and Patrick Lewis and Ledell Wu and Sergey Edunov and Danqi Chen and Wen-tau Yih},
  title     = {Dense Passage Retrieval for Open-Domain QA},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP 2020)},
  year      = {2020},
  eprint    = {2004.04906},
  archivePrefix = {arXiv},
  url       = {https://arxiv.org/abs/2004.04906}
}

Ejentic did not author this paper. Credit belongs to the authors named above; the note is ours.

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