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A growing body of research has demonstrated the inability of NLP models to generalize compositionally and has tried to alleviate it through specialized architectures, training schemes, and data augmentation, among other approaches.
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Deep bayesian active learning with image data
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Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks
Brenden M. Lake and Marco Baroni. 2018 · 2018
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Rearranging the familiar: Testing compositional generalization in recurrent networks
João Loula, Marco Baroni, and Brenden Lake. 2018 · 2018
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2018 · 2018
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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
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Don’t paraphrase, detect! rapid and effective data collection for semantic parsing
Jonathan Herzig and Jonathan Berant. 2019 · 2019
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Compositional generalization through meta sequence-to-sequence learning
Brenden M. Lake. 2019 · 2019
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Practical obstacles to deploying active learning
David Lowell, Zachary C. Lipton, and Byron C. Wallace. 2019 · 2019
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Trick me if you can: Human-in-the-loop generation of adversarial examples for question answering
Eric Wallace, Pedro Rodriguez, Shi Feng, Ikuya Yamada, and Jordan Boyd-Graber. 2019 · 2019
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Good-enough compositional data augmentation
Jacob Andreas. 2020 · 2020
Cited alongside, same era.
Task-oriented dialogue as dataflow synthesis
Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, Hao Fang, Alan Guo, David Hall, Kristin Hayes, Kellie Hill, Diana Ho, Wendy Iwaszuk, Smriti Jha, Dan Klein, Jayant Krishnamurthy, Theo Lanman, Percy Liang, Christopher H. Lin, Ilya Lintsbakh, Andy McGovern, Aleksandr Nisnevich, Adam Pauls, Dmitrij Petters, Brent Read, Dan Roth, Subhro Roy, Jesse Rusak, Beth Short, Div Slomin, Ben Snyder, Stephon Striplin, Yu Su, Zachary Tellman, Sam Thomson, Andrei Vorobev, Izabela Witoszko, Jason Wolfe, Abby Wray, Yuchen Zhang, and Alexander Zotov. 2020 · 2020
Cited alongside, same era.
Adversarial filters of dataset biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, and Yejin Choi. 2020 · 2020
Cited alongside, same era.
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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020 · 2020
Cited alongside, same era.
Schema2qa: High-quality and low-cost q&a agents for the structured web
Silei Xu, Giovanni Campagna, Jian Li, and Monica S. Lam. 2020 · 2020
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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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Meta-learning to compositionally generalize
Henry Conklin, Bailin Wang, Kenny Smith, and Ivan Titov. 2021 · 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
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Zero-shot transfer learning with synthesized data for multi-domain dialogue state tracking
Giovanni Campagna, Agata Foryciarz, Mehrad Moradshahi, and Monica Lam. 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.
Underspecification presents challenges for credibility in modern machine learning
Alexander D’Amour, Katherine Heller, Dan Moldovan, Ben Adlam, Babak Alipanahi, Alex Beutel, Christina Chen, Jonathan Deaton, Jacob Eisenstein, Matthew D. Hoffman, Farhad Hormozdiari, Neil Houlsby, Shaobo Hou, Ghassen Jerfel, Alan Karthikesalingam, Mario Lucic, Yian Ma, Cory McLean, Diana Mincu, Akinori Mitani, Andrea Montanari, Zachary Nado, Vivek Natarajan, Christopher Nielson, Thomas F. Osborne, Rajiv Raman, Kim Ramasamy, Rory Sayres, Jessica Schrouff, Martin Seneviratne, Shannon Sequeira, Harini Suresh, Victor Veitch, Max Vladymyrov, Xuezhi Wang, Kellie Webster, Steve Yadlowsky, Taedong Yun, Xiaohua Zhai, and D. Sculley. 2020 · 2020
Cited alongside, same era.
Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. 2020 · 2020
Cited alongside, same era.
Permutation equivariant models for compositional generalization in language
Jonathan Gordon, David Lopez-Paz, Marco Baroni, and Diane Bouchacourt. 2020 · 2020
Cited alongside, same era.
Sequence-level mixed sample data augmentation
Demi Guo, Yoon Kim, and Alexander Rush. 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.
COGS: A compositional generalization challenge based on semantic interpretation
Najoung Kim and Tal Linzen. 2020 · 2020
Cited alongside, same era.
Yinuo Guo, Hualei Zhu, Zeqi Lin, Bei Chen, Jian-Guang Lou, and Dongmei Zhang. 2021 · 2021
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Span-based semantic parsing for compositional generalization
Jonathan Herzig and Jonathan Berant. 2021 · 2021
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Mind your outliers! investigating the negative impact of outliers on active learning for visual question answering
Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, and Christopher Manning. 2021 · 2021
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Dynabench: Rethinking benchmarking in NLP
Douwe Kiela, Max Bartolo, Yixin Nie, Divyansh Kaushik, Atticus Geiger, Zhengxuan Wu, Bertie Vidgen, Grusha Prasad, Amanpreet Singh, Pratik Ringshia, Zhiyi Ma, Tristan Thrush, Sebastian Riedel, Zeerak Waseem, Pontus Stenetorp, Robin Jia, Mohit Bansal, Christopher Potts, and Adina Williams. 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
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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
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Learning to synthesize data for semantic parsing
Bailin Wang, Wenpeng Yin, Xi Victoria Lin, and Caiming Xiong. 2021 · 2021
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Compositional generalization for neural semantic parsing via span-level supervised attention
Pengcheng Yin, Hao Fang, Graham Neubig, Adam Pauls, Emmanouil Antonios Platanios, Yu Su, Sam Thomson, and Jacob Andreas. 2021 · 2021
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Unobserved local structures make compositional generalization hard
Ben Bogin, Shivanshu Gupta, and Jonathan Berant. 2022 · 2022
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Improving compositional generalization with latent structure and data augmentation
Linlu Qiu, Peter Shaw, Panupong Pasupat, Pawel Nowak, Tal Linzen, Fei Sha, and Kristina Toutanova. 2022 · 2022
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Active learning helps pretrained models learn the intended task
Alex Tamkin, Dat Nguyen, Salil Deshpande, Jesse Mu, and Noah Goodman. 2022 · 2022
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On the ingredients of an effective zero-shot semantic parser
Pengcheng Yin, John Wieting, Avirup Sil, and Graham Neubig. 2022 · 2022
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