Fetching the paper…
Reading the bibliography…
Answering questions that involve multi-step reasoning requires decomposing them and using the answers of intermediate steps to reach the final answer.
CLOSURE: assessing systematic generalization of CLEVR models
Dzmitry Bahdanau, Harm de Vries, Timothy J. O’Donnell, Shikhar Murty, Philippe Beaudoin, Yoshua Bengio, and Aaron C. Courville. 2019b · 1912
Earlier work this paper cites.
Syntactic Structures
Noam Chomsky. 1957 · 1957
Earlier work this paper cites.
An efficient recognition and syntax analysis algorithm for context-free languages
T. Kasami. 1965 · 1965
Earlier work this paper cites.
Recognition and parsing of context-free languages in time n 3 n^{3}
D.H. Younger. 1967 · 1967
Earlier work this paper cites.
Programming Languages and Their Compilers: Preliminary Notes
John Cocke. 1969 · 1969
Earlier work this paper cites.
Universal grammar
Richard Montague. 1970 · 1970
Earlier work this paper cites.
Connectionism and Cognitive Architecture: A Critical Analysis . MIT Press, Cambridge, MA, USA
Jerry A. Fodor and Zenon W. Pylyshyn. 1988 · 1988
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017a · 1997
Earlier work this paper cites.
A benchmark for systematic generalization in grounded language understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M. Lake. 2020 · 2003
Earlier work this paper cites.
On the value of out-of-distribution testing: An example of goodhart’s law
Damien Teney, Kushal Kafle, Robik Shrestha, Ehsan Abbasnejad, Christopher Kanan, and Anton van den Hengel. 2020 · 2005
Earlier work this paper cites.
Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars
Luke Zettlemoyer and Michael Collins. 2005 · 2007
Earlier work this paper cites.
Learning dependency-based compositional semantics
Percy Liang, Michael I. Jordan, and Dan Klein. 2013 · 2013
Earlier work this paper cites.
GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Earlier work this paper cites.
Wojciech Zaremba and Ilya Sutskever. 2014 · 2014
Earlier work this paper cites.
The forest convolutional network: Compositional distributional semantics with a neural chart and without binarization
Phong Le and Willem Zuidema. 2015 · 2015
Cited alongside, same era.
Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun. 2015 · 2015
Cited alongside, same era.
Neural module networks
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016 · 2016
Cited alongside, same era.
Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. 2016 · 2016
Cited alongside, same era.
Neural GPUs learn algorithms
Lukasz Kaiser and Ilya Sutskever. 2016 · 2016
Cited alongside, same era.
Inferring and executing programs for visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Judy Hoffman, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. 2017b · 2017
FiLM: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron Courville. 2018 · 2018
Later among the works it cites.
Neural arithmetic logic units
Andrew Trask, Felix Hill, Scott E Reed, Jack Rae, Chris Dyer, and Phil Blunsom. 2018 · 2018
Later among the works it cites.
Neural-symbolic VQA: Disentangling reasoning from vision and language understanding
Kexin Yi, Jiajun Wu, Chuang Gan, Antonio Torralba, Pushmeet Kohli, and Josh Tenenbaum. 2018 · 2018
Later among the works it cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
Unsupervised latent tree induction with deep inside-outside recursive auto-encoders
Andrew Drozdov, Patrick Verga, Mohit Yadav, Mohit Iyyer, and Andrew McCallum. 2019 · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Bottom-up and top-down attention for image captioning and visual question answering
Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, and Lei Zhang. 2018 · 2018
Cited alongside, same era.
Improving text-to-SQL evaluation methodology
Catherine Finegan-Dollak, Jonathan K. Kummerfeld, Li Zhang, Karthik Ramanathan, Sesh Sadasivam, Rui Zhang, and Dragomir Radev. 2018 · 2018
Cited alongside, same era.
Neural compositional denotational semantics for question answering
Nitish Gupta and Mike Lewis. 2018 · 2018
Cited alongside, same era.
Explainable neural computation via stack neural module networks
Ronghang Hu, Jacob Andreas, Trevor Darrell, and Kate Saenko. 2018 · 2018
Cited alongside, same era.
Compositional attention networks for machine reasoning
Drew Arad Hudson and Christopher D. Manning. 2018 · 2018
Cited alongside, same era.
Avoiding reasoning shortcuts: Adversarial evaluation, training, and model development for multi-hop QA
Yichen Jiang and Mohit Bansal. 2019 · 2019
Later among the works it cites.
Jointly learning sentence embeddings and syntax with unsupervised tree-LSTMs
Jean Maillard, Stephen Clark, and Dani Yogatama. 2019 · 2019
Later among the works it cites.
The neuro-symbolic concept learner: Interpreting scenes, words, and sentences from natural supervision
Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, and Jiajun Wu. 2019 · 2019
Later among the works it cites.
Analyzing compositionality in visual question answering
Sanjay Subramanian, Sameer Singh, and Matt Gardner. 2019 · 2019
Later among the works it cites.
LXMERT: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 2019
Later among the works it cites.
UNITER: Universal image-text representation learning
Yen-Chun Chen, Linjie Li, Licheng Yu, A. E. Kholy, Faisal Ahmed, Zhe Gan, Y. Cheng, and Jing jing Liu. 2020 · 2020
Closest in time.
Injecting numerical reasoning skills into language models
Mor Geva, Ankit Gupta, and Jonathan Berant. 2020 · 2020
Closest in time.
Measuring compositional generalization: A comprehensive method on realistic data
Daniel Keysers, Nathanael Schärli, Nathan Scales, Hylke Buisman, Daniel Furrer, Sergii Kashubin, Nikola Momchev, Danila Sinopalnikov, Lukasz Stafiniak, Tibor Tihon, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020 · 2020
Closest in time.
How can we accelerate progress towards human-like linguistic generalization?
Tal Linzen. 2020 · 2020
Closest in time.
Obtaining faithful interpretations from compositional neural networks
Sanjay Subramanian, Ben Bogin, Nitish Gupta, Tomer Wolfson, Sameer Singh, Jonathan Berant, and Matt Gardner. 2020 · 2020
Closest in time.