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State-of-the-art machine learning methods exhibit limited compositional generalization.
GQA: A new dataset for real-world visual reasoning and compositional question answering
Drew A. Hudson and Christopher D. Manning · 1902
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Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B Tenenbaum, and Jiajun Wu · 1904
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Compositional generalization in a deep seq2seq model by separating syntax and semantics
Jake Russin, Jason Jo, Randall C. O’Reilly, and Yoshua Bengio · 1904
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Learning by abstraction: The neural state machine
Drew A. Hudson and Christopher D. Manning · 1907
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Aspects of the Theory of Syntax
Noam Chomsky · 1965
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Features and values
Lauri Karttunen · 1984
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An introduction to unification-based approaches to grammar
Stuart M Shieber · 1986
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Connectionism and cognitive architecture: A critical analysis
Jerry A Fodor and Zenon W Pylyshyn · 1988
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Measures of distance between probability distributions
JK Chung, PL Kannappan, CT Ng, and PK Sahoo · 1989
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Gulp 3.1: An extension of prolog for unification-based grammar
Michael A. Covington · 1994
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The description logic handbook: Theory, implementation and applications
Franz Baader, Diego Calvanese, Deborah McGuinness, Peter Patel-Schneider, and Daniele Nardi · 2003
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Pushing the EL envelope
Franz Baader, Sebastian Brandt, and Carsten Lutz · 2005
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Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor · 2008
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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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Information extraction over structured data: Question answering with freebase
Xuchen Yao and Benjamin Van Durme · 2014
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2015
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang · 2015
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Key-value memory networks for directly reading documents
Alexander Miller, Adam Fisch, Jesse Dodge, Amir-Hossein Karimi, Antoine Bordes, and Jason Weston · 2016
Cited alongside, same era.
On generating characteristic-rich question sets for QA evaluation
Improving text-to-SQL evaluation methodology
Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev · 2018
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Scan: Learning hierarchical compositional visual concepts
Irina Higgins, Nicolas Sonnerat, Loic Matthey, Arka Pal, Christopher P Burgess, Matko Bosnjak, Murray Shanahan, Matthew Botvinick, Demis Hassabis, and Alexander Lerchner · 2018
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Compositional attention networks for machine reasoning
Drew A. Hudson and Christopher D. Manning · 2018
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Brenden M. Lake and Marco Baroni · 2018
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Rearranging the familiar: Testing compositional generalization in recurrent networks
João Loula, Marco Baroni, and Brenden Lake · 2018
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Yu Su, Huan Sun, Brian Sadler, Mudhakar Srivatsa, Izzeddin Gur, Zenghui Yan, and Xifeng Yan · 2016
Cited alongside, same era.
Towards AI-complete question answering: A set of prerequisite toy tasks
Jason Weston, Antoine Bordes, Sumit Chopra, Alexander M Rush, Bart van Merriënboer, Armand Joulin, and Tomas Mikolov · 2016
Cited alongside, same era.
CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Fei-Fei Li, Lawrence C. Zitnick, and Ross Girshick · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Don’t just assume; look and answer: Overcoming priors for visual question answering
Aishwarya Agrawal, Dhruv Batra, Devi Parikh, and Aniruddha Kembhavi · 2018
Cited alongside, same era.
Jump to better conclusions: SCAN both left and right
Jasmijn Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, and Douwe Kiela · 2018
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica Blake Chandler Hamrick, Victor Bapst, Alvaro Sanchez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andy Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Jayne Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Cited alongside, same era.
The web as a knowledge-base for answering complex questions
Alon Talmor and Jonathan Berant · 2018
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Tensor2tensor for neural machine translation
Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, and Jakob Uszkoreit · 2018
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Variational reasoning for question answering with knowledge graph
Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J Smola, and Le Song · 2018
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Measuring compositionality in representation learning
Jacob Andreas · 2019
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Systematic generalization: What is required and can it be learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch, Thien Huu Nguyen, Harm de Vries, and Aaron Courville · 2019
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Automatically composing representation transformations as a means for generalization
Michael Chang, Abhishek Gupta, Sergey Levine, and Thomas L. Griffiths · 2019
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Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
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The value of semantic parse labeling for knowledge base question answering
Scott Wen-tau Yih, Matthew Richardson, Chris Meek, Ming-Wei Chang, and Jina Suh · 2033
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