Dense Passage Retrieval for Open-Domain Question Answering

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We can see that higher retriever accuracy typically leads to better final QA results: in all cases except SQuAD, answers extracted from the passages retrieved by Dense Passage Retriever are more likely to be correct, compared to those from BM25

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...More

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