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Existing table question answering datasets contain abundant factual questions that primarily evaluate the query and schema comprehension capability of a system, but they fail to include questions that require complex reasoning and integration of information due to the constraint of the associated short-form answers.
Tabfact: A large-scale dataset for table-based fact verification
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Question and answer test-train overlap in open-domain question answering datasets
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Kilt: a benchmark for knowledge intensive language tasks
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Grappa: Grammar-augmented pre-training for table semantic parsing
Tao Yu, Chien-Sheng Wu, Xi Victoria Lin, Bailin Wang, Yi Chern Tan, Xinyi Yang, Dragomir Radev, Richard Socher, and Caiming Xiong. 2020 · 2009
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Open question answering over tables and text
Wenhu Chen, Ming-Wei Chang, Eva Schlinger, W. Wang, and William W. Cohen. 2020a · 2010
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Information extraction over structured data: Question answering with Freebase
Xuchen Yao and Benjamin Van Durme. 2014 · 2014
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Compositional semantic parsing on semi-structured tables
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Ms marco: A human generated machine reading comprehension dataset
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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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Reading Wikipedia to answer open-domain questions
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Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, and Edward Grefenstette. 2017 · 2017
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Coqa: A conversational question answering challenge
Siva Reddy, Danqi Chen, and Christopher D Manning. 2019 · 2019
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Wenhu Chen, Hanwen Zha, Zhiyu Chen, Wenhan Xiong, Hong Wang, and William Wang. 2020e · 2020
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Julian Eisenschlos, Syrine Krichene, and Thomas Müller. 2020 · 2020
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Hayate Iso, Yui Uehara, Tatsuya Ishigaki, Hiroshi Noji, Eiji Aramaki, Ichiro Kobayashi, Yusuke Miyao, Naoaki Okazaki, and Hiroya Takamura. 2020 · 2020
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ToTTo: A controlled table-to-text generation dataset
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The narrativeqa reading comprehension challenge
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Transformers: State-of-the-art natural language processing
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Pengcheng Yin, Graham Neubig, Wen-tau Yih, and Sebastian Riedel. 2020 · 2020
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Bertscore: Evaluating text generation with bert
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