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Reading comprehension has recently seen rapid progress, with systems matching humans on the most popular datasets for the task.
Learning to parse database queries using inductive logic programming
John M. Zelle and Raymond J. Mooney. 1996 · 1996
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Introduction to the conll-2005 shared task: Semantic role labeling
Xavier Carreras and Lluís Màrquez. 2005 · 2005
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Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Luke S. Zettlemoyer and Michael Collins. 2005 · 2005
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Open information extraction from the web
Michele Banko, Michael J. Cafarella, Stephen Soderland, Matthew G Broadhead, and Oren Etzioni. 2007 · 2007
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The stanford typed dependencies representation
Marie-Catherine de Marneffe and Christopher D. Manning. 2008 · 2008
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Learning to interpret natural language navigation instructions from observations
David L Chen and Raymond J Mooney. 2011 · 2011
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013b · 2013
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Learning to solve arithmetic word problems with verb categorization
Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, and Nate Kushman. 2014 · 2014
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Learning to automatically solve algebra word problems
Nate Kushman, Yoav Artzi, Luke Zettlemoyer, and Regina Barzilay. 2014 · 2014
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Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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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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Parsing algebraic word problems into equations
Rik Koncel-Kedziorski, Hannaneh Hajishirzi, Ashish Sabharwal, Oren Etzioni, and Siena Dumas Ang. 2015 · 2015
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang. 2015 · 2015
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A thorough examination of the cnn/daily mail reading comprehension task
Danqi Chen, Jason Bolton, and Christopher D. Manning. 2016 · 2016
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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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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V. Le, and Ilya Sutskever. 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
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Neural programmer-interpreters
Scott E. Reed and Nando de Freitas. 2016 · 2016
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Stanford’s graph-based neural dependency parser at the conll 2017 shared task
Timothy Dozat, Peng Qi, and Christopher D. Manning. 2017 · 2017
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Allennlp: A deep semantic natural language processing platform
Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke S. Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Deep semantic role labeling: What works and what’s next
Luheng He, Kenton Lee, Mike Lewis, and Luke S. Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar S. Joshi, Eunsol Choi, Daniel S. Weld, and Luke S. Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Neural semantic parsing with type constraints for semi-structured tables
Tracking state changes in procedural text: A challenge dataset and models for process paragraph comprehension
Bhavana Dalvi Mishra, Lifu Huang, Niket Tandon, Wen-tau Yih, and Peter Clark. 2018 · 2018
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Mcscript: a novel dataset for assessing machine comprehension using script knowledge
Simon Ostermann, Ashutosh Modi, Michael Roth, Stefan Thater, and Manfred Pinkal. 2018 · 2018
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emrqa: A large corpus for question answering on electronic medical records
Anusri Pampari, Preethi Raghavan, Jennifer Liang, and Jian Peng. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Know what you don’t know: Unanswerable questions for squad
Pranav Rajpurkar, Robin Jia, and Percy Liang. 2018 · 2018
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Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
Cited alongside, same era.
Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. 2017 · 2017
Cited alongside, same era.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
Cited alongside, same era.
Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
Cited alongside, same era.
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig. 2017 · 2017
Cited alongside, same era.
Quac: Question answering in context
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wen tau Yih, Yejin Choi, Percy Liang, and Luke S. Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Simple and effective multi-paragraph reading comprehension
Christopher Clark and Matt Gardner. 2018 · 2018
Cited alongside, same era.
Duorc: Towards complex language understanding with paraphrased reading comprehension
Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra, and Karthik Sankaranarayanan. 2018 · 2018
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Supervised open information extraction
Gabriel Stanovsky, Julian Michael, Luke S. Zettlemoyer, and Ido Dagan. 2018 · 2018
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Clicr: a dataset of clinical case reports for machine reading comprehension
Simon Šuster and Walter Daelemans. 2018 · 2018
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The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant. 2018 · 2018
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Constructing datasets for multi-hop reading comprehension across documents
Johannes Welbl, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. 2018 · 2018
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Qanet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 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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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D. Manning. 2019 · 2019
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From recognition to cognition: Visual commonsense reasoning
Rowan Zellers, Yonatan Bisk, Ali Farhadi, and Yejin Choi. 2019 · 2019
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ReCoRD: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme. 2019 · 2019
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