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Current Open-Domain Question Answering (ODQA) model paradigm often contains a retrieving module and a reading module.
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Knowledge guided text retrieval and reading for open domain question answering
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Realm: Retrieval-augmented language model pre-training
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Knowledge-aided open-domain question answering
Mantong Zhou, Zhouxing Shi, Minlie Huang, and Xiaoyan Zhu. 2020 · 2006
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Jaket: Joint pre-training of knowledge graph and language understanding
Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. 2020 · 2010
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Distilling knowledge from reader to retriever for question answering
Gautier Izacard and Edouard Grave. 2020 · 2012
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Unified open-domain question answering with structured and unstructured knowledge
Barlas Oguz, Xilun Chen, Vladimir Karpukhin, Stan Peshterliev, Dmytro Okhonko, Michael Schlichtkrull, Sonal Gupta, Yashar Mehdad, and Scott Yih. 2020 · 2012
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Semantic parsing on Freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Reading Wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer. 2017 · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Open domain question answering using early fusion of knowledge bases and text
Haitian Sun, Bhuwan Dhingra, Manzil Zaheer, Kathryn Mazaitis, Ruslan Salakhutdinov, and William Cohen. 2018 · 2018
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Graph attention networks
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R 3: Reinforced ranker-reader for open-domain question answering
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
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End-to-end open-domain question answering with BERTserini
Wei Yang, Yuqing Xie, Aileen Lin, Xingyu Li, Luchen Tan, Kun Xiong, Ming Li, and Jimmy Lin. 2019 · 2019
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Learning to retrieve reasoning paths over wikipedia graph for question answering
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
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Dense passage retrieval for open-domain question answering
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Natural questions: A benchmark for question answering research
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Multi-passage BERT: A globally normalized BERT model for open-domain question answering
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Retrieval-augmented generation for knowledge-intensive NLP tasks
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How much knowledge can you pack into the parameters of a language model?
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RECONSIDER: Improved re-ranking using span-focused cross-attention for open domain question answering
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