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We investigate the less-explored task of generating open-ended questions that are typically answered by multiple sentences.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 1903
Earlier work this paper cites.
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Does answering higher-level questions while reading facilitate productive learning?
Thomas Andre. 1979 · 1979
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Research on science laboratory activities: In pursuit of better questions and answers to improve learning
Kenneth Tobin. 1990 · 1990
Earlier work this paper cites.
Mechanisms that generate questions
Arthur C Graesser, Natalie Person, and John Huber. 1992 · 1992
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Steven M Downing and Rachel Yudkowsky. 2009 · 2009
Earlier work this paper cites.
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Samuel A Livingston. 2009 · 2009
Earlier work this paper cites.
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Michael Heilman and Noah A. Smith. 2010 · 2010
Earlier work this paper cites.
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Prashanth Mannem, Rashmi Prasad, and Aravind Joshi. 2010 · 2010
Earlier work this paper cites.
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Andrew M Olney, Arthur C Graesser, and Natalie K Person. 2012 · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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