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We present a novel method for obtaining high-quality, domain-targeted multiple choice questions from crowd workers.
Random forests
Leo Breiman. 2001 · 2001
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Computer-aided generation of multiple-choice tests
Ruslan Mitkov and Le An Ha. 2003 · 2003
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Measuring non-native speakers’ proficiency of english by using a test with automatically-generated fill-in-the-blank questions
Eiichiro Sumita, Fumiaki Sugaya, and Seiichi Yamamoto. 2005 · 2005
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Automatic generation of multiple choice questions from domain ontologies
Andreas Papasalouros, Konstantinos Kanaris, and Konstantinos Kotis. 2008 · 2008
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A Selection Strategy to Improve Cloze Question Quality
Juan Pino, Michael Heilman, and Maxine Eskenazi. 2008 · 2008
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Semantic similarity of distractors in multiple-choice tests: Extrinsic evaluation
Ruslan Mitkov, Le An Ha, Andrea Varga, and Luz Rello. 2009 · 2009
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Semi-automatic generation of cloze question distractors effect of students’ l1
Juan Pino and Maxine Eskénazi. 2009 · 2009
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Automatic Distractor Generation for Domain Specific Texts
Itziar Aldabe and Montse Maritxalar. 2010 · 2010
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Automatic generation of cloze question distractors
Rui Correia, Jorge Baptista, Nuno Mamede, Isabel Trancoso, and Maxine Eskenazi. 2010 · 2010
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Good question! statistical ranking for question generation
Michael Heilman and Noah A. Smith. 2010 · 2010
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Automatic gap-fill question generation from text books
Manish Agarwal and Prashanth Mannem. 2011 · 2011
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Answering elementary science questions by constructing coherent scenes using background knowledge
Yang Li and Peter Clark. 2015 · 2012
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Open language learning for information extraction
Mausam, Michael Schmitz, Robert Bart, Stephen Soderland, and Oren Etzioni. 2012 · 2012
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Generating diagnostic multiple choice comprehension cloze questions
Jack Mostow and Hyeju Jang. 2012 · 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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A study of the knowledge base requirements for passing an elementary science test
Peter Clark, Philip Harrison, and Niranjan Balasubramanian. 2013 · 2013
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Automatic generation of challenging distractors using context-sensitive inference rules
Torsten Zesch and Oren Melamud. 2014 · 2014
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Gated-attention readers for text comprehension
Bhuwan Dhingra, Hanxiao Liu, William W. Cohen, and Ruslan Salakhutdinov. 2016 · 2016
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Wikireading: A novel large-scale language understanding task over wikipedia
Daniel Hewlett, Alexandre Lacoste, Llion Jones, Illia Polosukhin, Andrew Fandrianto, Jay Han, Matthew Kelcey, and David Berthelot. 2016 · 2016
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Text understanding with the attention sum reader network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, and Jan Kleindienst. 2016 · 2016
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Question answering via integer programming over semi-structured knowledge
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, Peter Clark, Oren Etzioni, and Dan Roth. 2016 · 2016
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MS MARCO: A human generated machine reading comprehension dataset
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Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015 · 2015
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015 · 2015
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Elementary school science and math tests as a driver for ai: Take the aristo challenge!
Peter Clark. 2015 · 2015
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Teaching machines to read and comprehend
Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
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The goldilocks principle: Reading children’s books with explicit memory representations
Felix Hill, Antoine Bordes, Sumit Chopra, and Jason Weston. 2015 · 2015
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Exploring markov logic networks for question answering
Tushar Khot, Niranjan Balasubramanian, Eric Gribkoff, Ashish Sabharwal, Peter Clark, and Oren Etzioni. 2015 · 2015
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Wikiqa: A challenge dataset for open-domain question answering
Yi Yang, Scott Wen-tau Yih, and Chris Meek. 2015 · 2015
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Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
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Who did what: A large-scale person-centered cloze dataset
Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David A. McAllester. 2016 · 2016
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The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Quan Ngoc Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernández. 2016 · 2016
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Squad: 100,000+ questions for machine comprehension of text
P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang. 2016 · 2016
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Science question answering using instructional materials
Mrinmaya Sachan, Avinava Dubey, and Eric P. Xing. 2016 · 2016
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Moving beyond the turing test with the allen ai science challenge
Carissa Schoenick, Peter Clark, Oyvind Tafjord, Peter Turney, and Oren Etzioni. 2016 · 2016
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Iterative alternating neural attention for machine reading
Alessandro Sordoni, Phillip Bachman, and Yoshua Bengio. 2016 · 2016
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Discriminative approach to fill-in-the-blank quiz generation for language learners
Keisuke Sakaguchi, Yuki Arase, and Mamoru Komachi. 2013 · 2043
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