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Explainable observer-classifier for explainable binary decisions
Stephan Alaniz and Zeynep Akata. 2019 · 1902
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Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 1904
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Measuring bias in contextualized word representations
Keita Kurita, Nidhi Vyas, Ayush Pareek, Alan W Black, and Yulia Tsvetkov. 2019 · 1906
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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 · 1907
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ALBERT: A lite BERT for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2019 · 1909
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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Huggingface’s transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, R’emi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, Tommaso Caselli, Gertjan van Noord, and Malvina Nissim. 2019 · 1912
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Papers in Linguistics 1934-1951: Repr
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Natural language input for a computer problem solving system
Daniel G Bobrow. 1964 · 1964
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Computer solution of calculus word problems
Eugene Charniak. 1969 · 1969
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Linguistic and cultural influences on learning mathematics
Rodney R Cocking, Rodney T Cocking, and Jose P Mestre. 1988 · 1988
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What computers still can’t do: A critique of artificial reason
Hubert L Dreyfus, L Hubert, et al. 1992 · 1992
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Working memory and intrusions of irrelevant information in a group of specific poor problem solvers
Maria Chiara Pasolunghi, Cesare Cornoldi, and Stephanie De Liberto. 1999 · 1999
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Do different dimensions of male high school students’ skills predict labor market success a decade later? evidence from the nlsy
Richard J Murnane, John B Willett, M Jay Braatz, and Yves Duhaldeborde. 2001 · 2001
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Explainable artificial intelligence for training and tutoring
H Chad Lane, Mark G Core, Michael Van Lent, Steve Solomon, and Dave Gomboc. 2005 · 2005
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Building explainable artificial intelligence systems
Mark G Core, H Chad Lane, Michael Van Lent, Dave Gomboc, Steve Solomon, and Milton Rosenberg. 2006 · 2006
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The cognitive correlates of third-grade skill in arithmetic, algorithmic computation, and arithmetic word problems
Lynn S Fuchs, Douglas Fuchs, Donald L Compton, Sarah R Powell, Pamela M Seethaler, Andrea M Capizzi, Christopher Schatschneider, and Jack M Fletcher. 2006 · 2006
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Ireen: Iterative reverse-engineering of black-box functions via neural program synthesis
Hossein Hajipour, Mateusz Malinowski, and Mario Fritz. 2020 · 2006
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Mathematical reasoning via self-supervised skip-tree training
Markus N Rabe, Dennis Lee, Kshitij Bansal, and Christian Szegedy. 2020 · 2006
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Problem solving and computational skill: Are they shared or distinct aspects of mathematical cognition?
Lynn S Fuchs, Douglas Fuchs, Karla Stuebing, Jack M Fletcher, Carol L Hamlett, and Warren Lambert. 2008 · 2008
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Visualizing data using t-sne
Laurens Van der Maaten and Geoffrey Hinton. 2008 · 2008
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Investigating gender bias in bert
Rishabh Bhardwaj, Navonil Majumder, and Soujanya Poria. 2020 · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012 · 2012
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Learning executable semantic parsers for natural language understanding
Percy Liang. 2016 · 2016
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Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli. 2016 · 2016
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Cognitive and linguistic predictors of mathematical word problems with and without irrelevant information
Amber Y Wang, Lynn S Fuchs, and Douglas Fuchs. 2016 · 2016
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Ask, attend and answer: Exploring question-guided spatial attention for visual question answering
Huijuan Xu and Kate Saenko. 2016 · 2016
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Stacked attention networks for image question answering
Zichao Yang, Xiaodong He, Jianfeng Gao, Li Deng, and Alex Smola. 2016 · 2016
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Explaining robot actions
Meghann Lomas, Robert Chevalier, Ernest Vincent Cross, Robert Christopher Garrett, John Hoare, and Michael Kopack. 2012 · 2012
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The role of language comprehension in mathematics and problem solving
Jose P Mestre. 2013 · 2013
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
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A multi-world approach to question answering about real-world scenes based on uncertain input
Mateusz Malinowski and Mario Fritz. 2014 · 2014
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Recurrent models of visual attention
Volodymyr Mnih, Nicolas Heess, Alex Graves, et al. 2014 · 2014
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015 · 2015
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Linking language to math success in an on-line course
Scott Crossley, Tiffany Barnes, Collin Lynch, and Danielle S McNamara. 2017 · 2017
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Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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Women also snowboard: Overcoming bias in captioning models
Lisa Anne Hendricks, Kaylee Burns, Kate Saenko, Trevor Darrell, and Anna Rohrbach. 2018 · 2018
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Learning visual question answering by bootstrapping hard attention
Mateusz Malinowski, Carl Doersch, Adam Santoro, and Peter Battaglia. 2018 · 2018
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Mathdqn: Solving arithmetic word problems via deep reinforcement learning
Lei Wang, Dongxiang Zhang, Lianli Gao, Jingkuan Song, Long Guo, and Heng Tao Shen. 2018 · 2018
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MathQA: Towards interpretable math word problem solving with operation-based formalisms
Aida Amini, Saadia Gabriel, Shanchuan Lin, Rik Koncel-Kedziorski, Yejin Choi, and Hannaneh Hajishirzi. 2019 · 2019
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 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 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Text2Math: End-to-end parsing text into math expressions
Yanyan Zou and Wei Lu. 2019 · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. 2020 · 2020
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Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
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