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Tables are often created with hierarchies, but existing works on table reasoning mainly focus on flat tables and neglect hierarchical tables.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
The measurement of observer agreement for categorical data
J Richard Landis and Gary G Koch · 1977
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J Williams · 1992
Earlier work this paper cites.
An automated approach for retrieving hierarchical data from html tables
Seung-Jin Lim and Yiu-Kai Ng · 1999
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
Earlier work this paper cites.
Learning to sportscast: a test of grounded language acquisition
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Learning semantic correspondences with less supervision
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Earlier work this paper cites.
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Earlier work this paper cites.
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Huan Sun, Hao Ma, Xiaodong He, Wen-tau Yih, Yu Su, and Xifeng Yan · 2016
Earlier work this paper cites.
Neural symbolic machines: Learning semantic parsers on freebase with weak supervision
Chen Liang, Jonathan Berant, Quoc Le, Kenneth D Forbus, and Ni Lao · 2017
Earlier work this paper cites.
Deepdesrt: Deep learning for detection and structure recognition of tables in document images
Sebastian Schreiber, Stefan Agne, Ivo Wolf, Andreas Dengel, and Sheraz Ahmed · 2017
Earlier work this paper cites.
Get to the point: Summarization with pointer-generator networks
Abigail See, Peter J Liu, and Christopher D Manning · 2017
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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A graph representation of semi-structured data for web question answering
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