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We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples.
Building a question answering test collection
Ellen M. Voorhees and Dawn M. Tice. 2000 · 2000
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MCTest: A challenge dataset for the open-domain machine comprehension of text
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Anthony Fader, Luke Zettlemoyer, and Oren Etzioni. 2014 · 2014
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Mandar Joshi, Uma Sawant, and Soumen Chakrabarti. 2014 · 2014
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Antoine Bordes, Nicolas Usunier, Sumit Chopra, and Jason Weston. 2015 · 2015
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Karl Moritz Hermann, Tomáš Kočiský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015 · 2015
Dataset and neural recurrent sequence labeling model for open-domain factoid question answering
Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie Zhou, and Wei Xu. 2016 · 2016
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MS MARCO: A human generated machine reading comprehension dataset
Tri Nguyen, Mir Rosenberg, Xia Song, Jianfeng Gao, Saurabh Tiwary, Rangan Majumder, and Li Deng. 2016 · 2016
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Takeshi Onishi, Hai Wang, Mohit Bansal, Kevin Gimpel, and David McAllester. 2016 · 2016
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The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, and Raquel Fernandez. 2016 · 2016
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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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Hierarchical attention networks for document classification
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Searchqa: A new q&a dataset augmented with context from a search engine
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