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Generation-Augmented Retrieval for Open-domain Question Answering
[article]
2021
arXiv
pre-print
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts without external resources as supervision. We demonstrate that the generated contexts substantially enrich the semantics of the queries and GAR with sparse representations (BM25) achieves comparable or better performance than state-of-the-art dense retrieval methods such as DPR. We show that generating diverse contexts
arXiv:2009.08553v4
fatcat:3bcgxkx66fbotloyqp6rugbnze