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Open book question answering is a type of natural language based QA (NLQA) where questions are expected to be answered with respect to a given set of open book facts, and common knowledge about a topic.
Frame activated inferences in a story understanding program
Peter Norvig. 1983 · 1983
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Planning and understanding: A computational approach to human reasoning
Robert Wilensky. 1983 · 1983
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Inference in text understanding
Peter Norvig. 1987 · 1987
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A logic for semantic interpretation
Eugene Charniak and Robert Goldman. 1988 · 1988
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A semantics for probabilistic quantifier-free first-order languages, with particular application to story understanding
Eugene Charniak and Robert P Goldman. 1989 · 1989
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Interpretation as abduction
Jerry R Hobbs, Mark E Stickel, Douglas E Appelt, and Paul Martin. 1993 · 1993
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Open Book Assessment in Computing Degree Programmes
Tony Jenkins. 1995 · 1995
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Study guides and strategies
J Landsberger. 1996 · 1996
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The berkeley unix consultant project
Robert Wilensky, David N Chin, Marc Luria, James Martin, James Mayfield, and Dekai Wu. 2000 · 2000
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Abduction in natural language understanding
Jerry R Hobbs. 2004 · 2004
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Mctest: A challenge dataset for the open-domain machine comprehension of text
Matthew Richardson, Christopher JC Burges, and Erin Renshaw. 2013 · 2013
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Combining retrieval, statistics, and inference to answer elementary science questions
Peter Clark, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Oyvind Tafjord, Peter Turney, and Daniel Khashabi. 2016 · 2016
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Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 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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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani. 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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Looking beyond the surface: A challenge set for reading comprehension over multiple sentences
Daniel Khashabi, Snigdha Chaturvedi, Michael Roth, Shyam Upadhyay, and Dan Roth. 2018 · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal. 2018 · 2018
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Answering science exam questions using query rewriting with background knowledge
Ryan Musa, Xiaoyan Wang, Achille Fokoue, Nicholas Mattei, Maria Chang, Pavan Kapanipathi, Bassem Makni, Kartik Talamadupula, and Michael Witbrock. 2018 · 2018
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Jianmo Ni, Chenguang Zhu, Weizhu Chen, and Julian McAuley. 2018 · 2018
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Learning what is essential in questions
Daniel Khashabi, Tushar Khot, Ashish Sabharwal, and Dan Roth. 2017 · 2017
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Race: Large-scale reading comprehension dataset from examinations
Guokun Lai, Qizhe Xie, Hanxiao Liu, Yiming Yang, and Eduard Hovy. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen-tau Yih, Yejin Choi, Percy Liang, and Luke Zettlemoyer. 2018 · 2018
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Transforming question answering datasets into natural language inference datasets
Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Siva Reddy, Danqi Chen, and Christopher D Manning. 2018 · 2018
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Improving machine reading comprehension with general reading strategies
Kai Sun, Dian Yu, Dong Yu, and Claire Cardie. 2018 · 2018
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Quarel: A dataset and models for answering questions about qualitative relationships
Oyvind Tafjord, Peter Clark, Matt Gardner, Wen-tau Yih, and Ashish Sabharwal. 2018 · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2018 · 2018
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Swag: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
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Kgˆ 2: Learning to reason science exam questions with contextual knowledge graph embeddings
Yuyu Zhang, Hanjun Dai, Kamil Toraman, and Le Song. 2018 · 2018
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Declarative question answering over knowledge bases containing natural language text with answer set programming
Arindam Mitra, Peter Clark, Oyvind Tafjord, and Chitta Baral. 2019 · 2019
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