Dense Passage Retrieval for Open-Domain Question Answering (Research Paper Walkthrough)

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 see that retrieval can be practically implemented using dense representations achieving SOTA results.
Paper summary: https://lnkd.in/dzRmaqvX
Paper link: https://arxiv.org/abs/2004.04906

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