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While recent work has convincingly showed that sequence-to-sequence models struggle to generalize to new compositions (termed compositional generalization), little is known on what makes compositional generalization hard on a particular test instance.
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. 2019 · 1912
Earlier work this paper cites.
The atis spoken language systems pilot corpus
Charles T. Hemphill, John J. Godfrey, and George R. Doddington. 1990 · 1990
Earlier work this paper cites.
Expanding the scope of the ATIS task: The ATIS-3 corpus
Deborah A. Dahl, Madeleine Bates, Michael Brown, William Fisher, Kate Hunicke-Smith, David Pallett, Christine Pao, Alexander Rudnicky, and Elizabeth Shriberg. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
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Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
Daniel Furrer, Marc van Zee, Nathan Scales, and Nathanael Schärli. 2020 · 2007
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Learning dependency-based compositional semantics
Percy Liang, Michael Jordan, and Dan Klein. 2011 · 2011
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Incorporating copying mechanism in sequence-to-sequence learning
Jiatao Gu, Zhengdong Lu, Hang Li, and Victor O.K. Li. 2016 · 2016
Earlier work this paper cites.
Jump to better conclusions: SCAN both left and right
Jasmijn Bastings, Marco Baroni, Jason Weston, Kyunghyun Cho, and Douwe Kiela. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden Lake and Marco Baroni. 2018 · 2018
Cited alongside, same era.
Genie: A generator of natural language semantic parsers for virtual assistant commands
Giovanni Campagna, Silei Xu, Mehrad Moradshahi, Richard Socher, and Monica S. Lam. 2019 · 2019
Cited alongside, same era.
Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
Cited alongside, same era.
Compositional generalization via neural-symbolic stack machines
Xinyun Chen, Chen Liang, Adams Wei Yu, Dawn Song, and Denny Zhou. 2020 · 2020
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, Dmitry Tsarkov, Xiao Wang, Marc van Zee, and Olivier Bousquet. 2020 · 2020
Cited alongside, same era.
Silei Xu, Giovanni Campagna, Jian Li, and Monica S. Lam. 2020 · 2020
Later among the works it cites.
Learning to recombine and resample data for compositional generalization
Ekin Akyürek, Afra Feyza Akyürek, and Jacob Andreas. 2021 · 2021
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COVR: A test-bed for visually grounded compositional generalization with real images
Ben Bogin, Shivanshu Gupta, Matt Gardner, and Jonathan Berant. 2021a · 2021
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The devil is in the detail: Simple tricks improve systematic generalization of transformers
Róbert Csordás, Kazuki Irie, and Juergen Schmidhuber. 2021 · 2021
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Revisiting iterative back-translation from the perspective of compositional generalization
Yinuo Guo, Hualei Zhu, Zeqi Lin, Bei Chen, Jian-Guang Lou, and Dongmei Zhang. 2021 · 2021
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COGS: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen. 2020 · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020 · 2020
Cited alongside, same era.
Improving compositional generalization in semantic parsing
Inbar Oren, Jonathan Herzig, Nitish Gupta, Matt Gardner, and Jonathan Berant. 2020 · 2020
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. 2020 · 2020
Cited alongside, same era.
A benchmark for systematic generalization in grounded language understanding
Laura Ruis, Jacob Andreas, Marco Baroni, Diane Bouchacourt, and Brenden M Lake. 2020 · 2020
Cited alongside, same era.
Latent compositional representations improve systematic generalization in grounded question answering
Ben Bogin, Sanjay Subramanian, Matt Gardner, and Jonathan Berant. 2021b
Cited in the paper.
Later among the works it cites.
Span-based semantic parsing for compositional generalization
Jonathan Herzig and Jonathan Berant. 2021 · 2021
Later among the works it cites.
Unlocking compositional generalization in pre-trained models using intermediate representations
Jonathan Herzig, Peter Shaw, Ming-Wei Chang, Kelvin Guu, Panupong Pasupat, and Yuan Zhang. 2021 · 2021
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Finding needles in a haystack: Sampling structurally-diverse training sets from synthetic data for compositional generalization
Inbar Oren, Jonathan Herzig, and Jonathan Berant. 2021 · 2021
Later among the works it cites.
Compositional generalization and natural language variation: Can a semantic parsing approach handle both?
Peter Shaw, Ming-Wei Chang, Panupong Pasupat, and Kristina Toutanova. 2021 · 2021
Later among the works it cites.