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Open-domain question answering (QA) is an important problem in AI and NLP that is emerging as a bellwether for progress on the generalizability of AI methods and techniques.
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Paraphrase-driven learning for open question answering
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MCTest: A challenge dataset for the open-domain machine comprehension of text
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Elementary school science and math tests as a driver for AI: Take the Aristo Challenge!
P. Clark · 2015
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My computer is an honor student—but how intelligent is it? Standardized tests as a measure of AI
P. Clark and O. Etzioni · 2016
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Question answering via integer programming over semi-structured knowledge
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Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman · 2016
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Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
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J. Welbl, N. F. Liu, and M. Gardner · 2017
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SciTail: A textual entailment dataset from science question answering
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Query expansion techniques for information retrieval: a survey
Hiteshwar Kumar Azad and Akshay Deepak · 2017
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Ask the right questions: Active question reformulation with reinforcement learning
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes · 2017
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Learning to paraphrase for question answering
Li Dong, Jonathan Mallinson, Siva Reddy, and Mirella Lapata · 2017
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SearchQA: A new Q&A dataset augmented with context from a search engine
Matthew Dunn, Levent Sagun, Mike Higgins, Ugur Guney, Volkan Cirik, and Kyunghyun Cho · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer · 2017
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T. Khot, A. Sabharwal, and P. Clark · 2018
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