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Dense Passage Retrieval for Open-Domain Question Answering
[article]
2020
arXiv
pre-print
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
arXiv:2004.04906v3
fatcat:yff6ror4xbbjxjcoaaczsdzhqi