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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.
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How much knowledge can you pack into the parameters of a language model?
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Progressively pretrained dense corpus index for open-domain question answering
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Leveraging passage retrieval with generative models for open domain question answering
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Denoising distantly supervised open-domain question answering
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Rˆ3: Reinforced ranker-reader for open-domain question answering
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Multi-step retriever-reader interaction for scalable open-domain question answering
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BERT: Pre-training of deep bidirectional transformers for language understanding
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Learning dense representations for entity retrieval
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Asymmetric LSH (ALSH) for sublinear time maximum inner product search (MIPS)
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Modeling of the question answering task in the yodaqa system
Petr Baudiš and Jan Šedivỳ. 2015 · 2015
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Quantization based fast inner product search
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Natural questions: a benchmark for question answering research
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Latent retrieval for weakly supervised open domain question answering
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Revealing the importance of semantic retrieval for machine reading at scale
Yixin Nie, Songhe Wang, and Mohit Bansal. 2019 · 2019
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Real-time open-domain question answering with dense-sparse phrase index
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Multi-passage BERT: A globally normalized bert model for open-domain question answering
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Learning to retrieve reasoning paths over Wikipedia graph for question answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi, Richard Socher, and Caiming Xiong. 2020 · 2020
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