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Neural sequence models trained with maximum likelihood estimation have led to breakthroughs in many tasks, where success is defined by the gap between training and test performance.
Can Neural Networks Learn Symbolic Rewriting?
Piotrowski, B.; Urban, J.; Brown, C. E.; and Kaliszyk, C. 2019 · 1911
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On Compositionality in Neural Machine Translation
Raunak, V.; Kumar, V.; Metze, F.; and Callan, J. 2019 · 1911
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The Use of Deep Learning for Symbolic Integration: A Review of (Lample and Charton, 2019)
Davis, E. 2019 · 1912
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Deep learning for symbolic mathematics
Lample, G.; and Charton, F. 2019 · 1912
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Analyzing the Behavior of Visual Question Answering Models
Agrawal, A.; Batra, D.; and Parikh, D. 2016 · 1960
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Learning advanced mathematical computations from examples
Charton, F.; Hayat, A.; and Lample, G. 2021 · 2006
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Scaling Laws for Autoregressive Generative Modeling
Henighan, T.; Kaplan, J.; Katz, M.; Chen, M.; Hesse, C.; Jackson, J.; Jun, H.; Brown, T. B.; Dhariwal, P.; Gray, S.; Hallacy, C.; Mann, B.; Radford, A.; Ramesh, A.; Ryder, N.; Ziegler, D. M.; Schulman, J.; Amodei, D.; and McCandlish, S. 2020 · 2010
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Zaremba, W.; and Sutskever, I. 2014 · 2014
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Neural module networks
Andreas, J.; Rohrbach, M.; Darrell, T.; and Klein, D. 2016 · 2016
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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Generating Natural Language Adversarial Examples
Alzantot, M.; Sharma, Y.; Elgohary, A.; Ho, B.-J.; Srivastava, M.; and Chang, K.-W. 2018 · 2018
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Still Not Systematic After All These Years: On the Compositional Skills of Sequence-To-Sequence Recurrent Networks
Lake, B.; and Baroni, M. 2018 · 2018
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Neural arithmetic logic units
Trask, A.; Hill, F.; Reed, S.; Rae, J.; Dyer, C.; and Blunsom, P. 2018 · 2018
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Systematic generalization: What is required and can it be learned?
Bahdanau, D.; Murty, S.; Noukhovitch, M.; Nguyen, T. H.; De Vries, H.; and Courville, A. 2019 · 2019
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SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I.; Lo, K.; and Cohan, A. 2019 · 2019
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Certified Robustness to Adversarial Word Substitutions
Jia, R.; Raghunathan, A.; Göksel, K.; and Liang, P. 2019 · 2019
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Compositional generalization through meta sequence-to-sequence learning
Lake, B. M. 2019 · 2019
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On Evaluation of Adversarial Perturbations for Sequence-to-Sequence Models
Michel, P.; Li, X.; Neubig, G.; and Pino, J. 2019 · 2019
Environmental drivers of systematicity and generalization in a situated agent
Hill, F.; Lampinen, A.; Schneider, R.; Clark, S.; Botvinick, M.; McClelland, J. L.; and Santoro, A. 2020 · 2020
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The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2020 · 2020
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Compositionality Decomposed: How do Neural Networks Generalise?
Hupkes, D.; Dankers, V.; Mul, M.; and Bruni, E. 2020 · 2020
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Selective Question Answering under Domain Shift
Kamath, A.; Jia, R.; and Liang, P. 2020 · 2020
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COGS: A Compositional Generalization Challenge Based on Semantic Interpretation
Kim, N.; and Linzen, T. 2020 · 2020
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TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
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Distributionally Robust Language Modeling
Oren, Y.; Sagawa, S.; Hashimoto, T. B.; and Liang, P. 2019 · 2019
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Analysing Mathematical Reasoning Abilities of Neural Models
Saxton, D.; Grefenstette, E.; Hill, F.; and Kohli, P. 2019 · 2019
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Universal Adversarial Triggers for Attacking and Analyzing NLP
Wallace, E.; Feng, S.; Kandpal, N.; Gardner, M.; and Singh, S. 2019 · 2019
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Good-Enough Compositional Data Augmentation
Andreas, J. 2020 · 2020
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Evaluating Models’ Local Decision Boundaries via Contrast Sets
Gardner, M.; Artzi, Y.; Basmov, V.; Berant, J.; Bogin, B.; Chen, S.; Dasigi, P.; Dua, D.; Elazar, Y.; Gottumukkala, A.; Gupta, N.; Hajishirzi, H.; Ilharco, G.; Khashabi, D.; Lin, K.; Liu, J.; Liu, N. F.; Mulcaire, P.; Ning, Q.; Singh, S.; Smith, N. A.; Subramanian, S.; Tsarfaty, R.; Wallace, E.; Zhang, A.; and Zhou, B. 2020 · 2020
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Morris, J.; Lifland, E.; Yoo, J. Y.; Grigsby, J.; Jin, D.; and Qi, Y. 2020 · 2020
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An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Tu, L.; Lalwani, G.; Gella, S.; and He, H. 2020 · 2020
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Consistency of a Recurrent Language Model With Respect to Incomplete Decoding
Welleck, S.; Kulikov, I.; Kim, J.; Pang, R. Y.; and Cho, K. 2020 · 2020
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Analyzing the Nuances of Transformers’ Polynomial Simplification Abilities
Agarwal, V.; Aditya, S.; and Goyal, N. 2021 · 2021
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Investigating the Limitations of the Transformers with Simple Arithmetic Tasks
Nogueira, R.; Jiang, Z.; and Li, J. J. 2021 · 2021
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Iterated learning for emergent systematicity in {VQA}
Vani, A.; Schwarzer, M.; Lu, Y.; Dhekane, E.; and Courville, A. 2021 · 2021
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