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In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering.
Multi-hop paragraph retrieval for open-domain question answering
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Span selection pre-training for question answering
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Simple yet effective bridge reasoning for open-domain multi-hop question answering
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Multi-hop question answering via reasoning chains
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Transformers: State-of-the-art natural language processing
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Longformer: The long-document transformer
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Is graph structure necessary for multi-hop reasoningt
Nan Shao, Yiming Cui, Ting Liu, Wang, and Guoping Hu Hu. 2020 · 2004
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Big bird: Transformers for longer sequences
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Cluster-former: Clustering-based sparse transformer for long-range dependency encoding
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Newsqa: A machine comprehension dataset
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Reading Wikipedia to answer open-domain questions
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Coarse-to-fine question answering for long documents
Eunsol Choi, Daniel Hewlett, Jakob Uszkoreit, Illia Polosukhin, Alexandre Lacoste, and Jonathan Berant. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
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Bhuwan Dhingra, Qiao Jin, Zhilin Yang, William W Cohen, and Ruslan Salakhutdinov. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
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