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Tabular data is difficult to analyze and to search through, yielding for new tools and interfaces that would allow even non tech-savvy users to gain insights from open datasets without resorting to specialized data analysis tools or even without having to fully understand the dataset structure.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
End-to-end memory networks
Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. 2015 · 2015
Earlier work this paper cites.
Towards ai-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, and Tomas Mikolov. 2015 · 2015
Earlier work this paper cites.
Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2016 · 2016
Cited alongside, same era.
Learning a natural language interface with neural programmer
Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, and Dario Amodei. 2016 · 2016
Cited alongside, same era.
Table cell search for question answering
Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su, and Xifeng Yan. 2016 · 2016
Cited alongside, same era.
Neural enquirer: Learning to query tables in natural language
Pengcheng Yin, Zhengdong Lu, Hang Li, and Ben Kao. 2016 · 2016
Later among the works it cites.
Talking open data
Sebastian Neumaier, Vadim Savenkov, and Svitlana Vakulenko. 2017 · 2017
Closest in time.
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