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- W4226048044 abstract "Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR) -- a state-of-the-art (SOTA) open domain neural retrieval model -- on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets." @default.
- W4226048044 created "2022-05-05" @default.
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- W4226048044 date "2022-04-20" @default.
- W4226048044 modified "2023-09-27" @default.
- W4226048044 title "Synthetic Target Domain Supervision for Open Retrieval QA" @default.
- W4226048044 doi "https://doi.org/10.48550/arxiv.2204.09248" @default.
- W4226048044 hasPublicationYear "2022" @default.
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