Fetching the paper…
Reading the bibliography…
We propose PIGLeT: a model that learns physical commonsense knowledge through interaction, and then uses this knowledge to ground language.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, V. Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 1904
Earlier work this paper cites.
Unified language model pre-training for natural language understanding and generation
Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019 · 1905
Earlier work this paper cites.
Extending machine language models toward human-level language understanding
James L McClelland, Felix Hill, Maja Rudolph, Jason Baldridge, and Hinrich Schütze. 2019 · 1912
Earlier work this paper cites.
Pre-learning environment representations for data-efficient neural instruction following
David Gaddy and Dan Klein. 2019 · 1956
Earlier work this paper cites.
Movement-produced stimulation in the development of visually guided behavior
Richard Held and Alan Hein. 1963 · 1963
Earlier work this paper cites.
Acquiring a single new word
S. Carey and E. Bartlett. 1978 · 1978
Earlier work this paper cites.
The symbol grounding problem
Stevan Harnad. 1990 · 1990
Earlier work this paper cites.
Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020 · 2001
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 · 2002
Earlier work this paper cites.
Yonatan Bisk, Ari Holtzman, Jesse Thomason, Jacob Andreas, Yoshua Bengio, Joyce Chai, Mirella Lapata, Angeliki Lazaridou, Jonathan May, Aleksandr Nisnevich, et al. 2020 · 2004
Earlier work this paper cites.
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 · 2005
Earlier work this paper cites.
The development of embodied cognition: Six lessons from babies
Linda Smith and Michael Gasser. 2005 · 2005
Earlier work this paper cites.
A joint model of language and perception for grounded attribute learning
Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, and Dieter Fox. 2012 · 2012
Earlier work this paper cites.
Embodied attention and word learning by toddlers
Chen Yu and Linda B Smith. 2012 · 2012
Earlier work this paper cites.
Semantic parsing with combinatory categorial grammars
Yoav Artzi, Nicholas FitzGerald, and Luke S Zettlemoyer. 2013 · 2013
Cited alongside, same era.
Devise: A deep visual-semantic embedding model
Andrea Frome, Greg Corrado, Jonathon Shlens, Samy Bengio, Jeffrey Dean, Marc’Aurelio Ranzato, and Tomas Mikolov. 2013 · 2013
Cited alongside, same era.
Reporting bias and knowledge acquisition
Jonathan Gordon and Benjamin Van Durme. 2013 · 2013
Cited alongside, same era.
Jointly learning to parse and perceive: Connecting natural language to the physical world
Jayant Krishnamurthy and Thomas Kollar. 2013 · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Cited alongside, same era.
Actions ~ transformations
Xiaolong Wang, Ali Farhadi, and Abhinav Gupta. 2016 · 2016
SWAG: A large-scale adversarial dataset for grounded commonsense inference
Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018 · 2018
Later among the works it cites.
Fusion of detected objects in text for visual question answering
Chris Alberti, Jeffrey Ling, Michael Collins, and David Reitter. 2019 · 2019
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.
Effective use of transformer networks for entity tracking
Aditya Gupta and Greg Durrett. 2019 · 2019
Later among the works it cites.
Vico: Word embeddings from visual co-occurrences
Tanmay Gupta, Alexander Schwing, and Derek Hoiem. 2019 · 2019
Later among the works it cites.
The fast and the flexible: Training neural networks to learn to follow instructions from small data
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Stating the obvious: Extracting visual common sense knowledge
Mark Yatskar, Vicente Ordonez, and Ali Farhadi. 2016 · 2016
Cited alongside, same era.
Ai2-thor: An interactive 3d environment for visual ai
Eric Kolve, Roozbeh Mottaghi, Winson Han, Eli VanderBilt, Luca Weihs, Alvaro Herrasti, Daniel Gordon, Yuke Zhu, Abhinav Gupta, and Ali Farhadi. 2017 · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Zero-shot activity recognition with verb attribute induction
Rowan Zellers and Yejin Choi. 2017 · 2017
Cited alongside, same era.
Embodied question answering
Abhishek Das, Samyak Datta, Georgia Gkioxari, Stefan Lee, Devi Parikh, and Dhruv Batra. 2018 · 2018
Cited alongside, same era.
Iqa: Visual question answering in interactive environments
Daniel Gordon, Aniruddha Kembhavi, Mohammad Rastegari, Joseph Redmon, Dieter Fox, and Ali Farhadi. 2018 · 2018
Cited alongside, same era.
Rezka Leonandya, Dieuwke Hupkes, Elia Bruni, and Germán Kruszewski. 2019 · 2019
Later among the works it cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2019 · 2019
Later among the works it cites.
Climbing towards NLU: On meaning, form, and understanding in the age of data
Emily M. Bender and Alexander Koller. 2020 · 2020
Later among the works it cites.
Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020 · 2020
Later among the works it cites.
A benchmark for systematic generalization in grounded language understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M Lake. 2020 · 2020
Later among the works it cites.
Alfred: A benchmark for interpreting grounded instructions for everyday tasks
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox. 2020 · 2020
Later among the works it cites.
On the dangers of stochastic parrots: Can language models be too big
Emily M Bender, Timnit Gebru, Angelina McMillan-Major, and Shmargaret Shmitchell. 2021 · 2021
Closest in time.