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- W3179867916 abstract "Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also, multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA." @default.
- W3179867916 created "2021-07-19" @default.
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- W3179867916 date "2021-07-11" @default.
- W3179867916 modified "2023-10-11" @default.
- W3179867916 title "Answering Any-hop Open-domain Questions with Iterative Document Reranking" @default.
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- W3179867916 doi "https://doi.org/10.1145/3404835.3462853" @default.
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