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The problem of verifying whether a textual hypothesis holds based on the given evidence, also known as fact verification, plays an important role in the study of natural language understanding and semantic representation.
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang · 2013
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Methods for exploring and mining tables on wikipedia
Chandra Sekhar Bhagavatula, Thanapon Noraset, and Doug Downey · 2013
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Learning dependency-based compositional semantics
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Learning compact lexicons for ccg semantic parsing
Yoav Artzi, Dipanjan Das, and Slav Petrov · 2014
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Semantic parsing via paraphrasing
Jonathan Berant and Percy Liang · 2014
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Fact checking: Task definition and dataset construction
Andreas Vlachos and Sebastian Riedel · 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
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Language to logical form with neural attention
Li Dong and Mirella Lapata · 2016
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Tables as semi-structured knowledge for question answering
Sujay Kumar Jauhar, Peter Turney, and Eduard Hovy · 2016
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Neural programmer: Inducing latent programs with gradient descent
Arvind Neelakantan, Quoc V Le, and Ilya Sutskever · 2016
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A decomposable attention model for natural language inference
Ankur Parikh, Oscar Täckström, Dipanjan Das, and Jakob Uszkoreit · 2016
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Enhanced lstm for natural language inference
Qian Chen, Xiaodan Zhu, Zhen-Hua Ling, Si Wei, Hui Jiang, and Diana Inkpen · 2017
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Claimbuster: the first-ever end-to-end fact-checking system
Naeemul Hassan, Gensheng Zhang, Fatma Arslan, Josue Caraballo, Damian Jimenez, Siddhant Gawsane, Shohedul Hasan, Minumol Joseph, Aaditya Kulkarni, Anil Kumar Nayak, et al · 2017
A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
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Seq2sql: Generating structured queries from natural language using reinforcement learning
Victor Zhong, Caiming Xiong, and Richard Socher · 2017
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Ukp-athene: Multi-sentence textual entailment for claim verification
Andreas Hanselowski, Hao Zhang, Zile Li, Daniil Sorokin, Benjamin Schiller, Claudia Schulz, and Iryna Gurevych · 2018
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Memory augmented policy optimization for program synthesis and semantic parsing
Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc V Le, and Ni Lao · 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
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Search-based neural structured learning for sequential question answering
Mohit Iyyer, Wen-tau Yih, and Ming-Wei Chang · 2017
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Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao · 2017
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Learning a natural language interface with neural programmer
Arvind Neelakantan, Quoc V Le, Martin Abadi, Andrew McCallum, and Dario Amodei · 2017
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Where the truth lies: Explaining the credibility of emerging claims on the web and social media
Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen, and Gerhard Weikum · 2017
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Programming with a differentiable forth interpreter
Sebastian Riedel, Matko Bosnjak, and Tim Rocktäschel · 2017
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End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel · 2017
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Fever: a large-scale dataset for fact extraction and verification
James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2018
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Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task
Tao Yu, Rui Zhang, Kai Yang, Michihiro Yasunaga, Dongxu Wang, Zifan Li, James Ma, Irene Li, Qingning Yao, Shanelle Roman, et al · 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
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Learning to generalize from sparse and underspecified rewards
Rishabh Agarwal, Chen Liang, Dale Schuurmans, and Mohammad Norouzi · 2019
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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
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Aggchecker: A fact-checking system for text summaries of relational data sets
Saehan Jo, Immanuel Trummer, Weicheng Yu, Xuezhi Wang, Cong Yu, Daniel Liu, and Niyati Mehta · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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A corpus for reasoning about natural language grounded in photographs
Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, and Yoav Artzi · 2019
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V Le · 2019
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