Dense Passage Retrieval for Open-Domain Question Answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, Wen-tau Yih
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 greatly 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.- Anthology ID:
- 2020.emnlp-main.550
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6769–6781
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.550
- DOI:
- 10.18653/v1/2020.emnlp-main.550
- Cite (ACL):
- Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih. 2020. Dense Passage Retrieval for Open-Domain Question Answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 6769–6781, Online. Association for Computational Linguistics.
- Cite (Informal):
- Dense Passage Retrieval for Open-Domain Question Answering (Karpukhin et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.emnlp-main.550.pdf
- Code
- facebookresearch/DPR + additional community code
- Data
- Natural Questions, SQuAD, TriviaQA, WebQuestions