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Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A large annotated corpus for learning natural language inference
Samuel R Bowman, Gabor Angeli, Christopher Potts, and Christopher D Manning · 2015
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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Deep neural solver for math word problems
Yan Wang, Xiaojiang Liu, and Shuming Shi · 2017
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e-snli: Natural language inference with natural language explanations
Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, and Phil Blunsom · 2018
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni · 2018
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Memorize or generalize? searching for a compositional rnn in a haystack
Adam Liška, Germán Kruszewski, and Marco Baroni · 2018
Cited alongside, same era.
Adafactor: Adaptive learning rates with sublinear memory cost
Noam Shazeer and Mitchell Stern · 2018
Cited alongside, same era.
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, et al · 2019
Cited alongside, same era.
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
Cited alongside, same era.
Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli · 2020
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Compositionality decomposed: How do neural networks generalise?
Dieuwke Hupkes, Verna Dankers, Mathijs Mul, and Elia Bruni · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
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The devil is in the detail: Simple tricks improve systematic generalization of transformers
Róbert Csordás, Kazuki Irie, and Jürgen Schmidhuber · 2021
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David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli · 2019
Cited alongside, same era.
Clutrr: A diagnostic benchmark for inductive reasoning from text
Koustuv Sinha, Shagun Sodhani, Jin Dong, Joelle Pineau, and William L Hamilton · 2019
Cited alongside, same era.
Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, Zhengnan Xie, Hannah Smith, Leighanna Pipatanangkura, and Peter Clark · 2021
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Making transformers solve compositional tasks
Santiago Ontanón, Joshua Ainslie, Vaclav Cvicek, and Zachary Fisher · 2021
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