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Existing generative pre-trained language models (e.g., GPT) focus on modeling the language structure and semantics of general texts.
Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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Jiang, C.; Nian, Z.; Guo, K.; Chu, S.; Zhao, Y.; Shen, L.; and Tu, K. 2019 · 2001
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Learning to solve arithmetic word problems with verb categorization
Hosseini, M. J.; Hajishirzi, H.; Etzioni, O.; and Kushman, N. 2014 · 2014
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Solving general arithmetic word problems
Roy, S.; and Roth, D. 2015 · 2015
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Neural machine translation of rare words with subword units
Sennrich, R.; Haddow, B.; and Birch, A. 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, L.; and Polosukhin, I. 2017 · 2017
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Deep neural solver for math word problems
Wang, Y.; Liu, X.; and Shi, S. 2017 · 2017
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Neural math word problem solver with reinforcement learning
Huang, D.; Liu, J.; Lin, C.-Y.; and Yin, J. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; and Sutskever, I. 2018 · 2018
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Mathdqn: Solving arithmetic word problems via deep reinforcement learning
Wang, L.; Zhang, D.; Gao, L.; Song, J.; Guo, L.; and Shen, H. T. 2018 · 2018
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Mathqa: Towards interpretable math word problem solving with operation-based formalisms
Amini, A.; Gabriel, S.; Lin, P.; Koncel-Kedziorski, R.; Choi, Y.; and Hajishirzi, H. 2019 · 2019
Cited alongside, same era.
Numeracy-600k: Learning numeracy for detecting exaggerated information in market comments
Chen, C.-C.; Huang, H.-H.; Takamura, H.; and Chen, H.-H. 2019 · 2019
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Semantically-aligned equation generation for solving and reasoning math word problems
Chiang, T.-R.; and Chen, Y.-N. 2019 · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
Solving arithmetic word problems automatically using transformer and unambiguous representations
Griffith, K.; and Kalita, J. 2019 · 2019
Cited alongside, same era.
Do nlp models know numbers? Probing numeracy in embeddings
Wallace, E.; Wang, Y.; Li, S.; Singh, S.; and Gardner, M. 2019 · 2019
Later among the works it cites.
The gap of semantic parsing: A survey on automatic math word problem solvers
Zhang, D.; Wang, L.; Zhang, L.; Dai, B. T.; and Shen, H. T. 2019 · 2019
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An Empirical Investigation of Contextualized Number Prediction
Berg-Kirkpatrick, T.; and Spokoyny, D. 2020 · 2020
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Language models are few-shot learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 2020
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Injecting numerical reasoning skills into language models
Geva, M.; Gupta, A.; and Berant, J. 2020 · 2020
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Methods for Numeracy-Preserving Word Embeddings
Sundararaman, D.; Si, S.; Subramanian, V.; Wang, G.; Hazarika, D.; and Carin, L. 2020 · 2020
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Albert: A lite bert for self-supervised learning of language representations
Lan, Z.; Chen, M.; Goodman, S.; Gimpel, K.; Sharma, P.; and Soricut, R. 2019 · 2019
Cited alongside, same era.
Exploring numeracy in word embeddings
Naik, A.; Ravichander, A.; Rose, C.; and Hovy, E. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Cited alongside, same era.
EQUATE: A benchmark evaluation framework for quantitative reasoning in natural language inference
Ravichander, A.; Naik, A.; Rose, C.; and Hovy, E. 2019 · 2019
Cited alongside, same era.
Superglue: A stickier benchmark for general-purpose language understanding systems
Wang, A.; Pruksachatkun, Y.; Nangia, N.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. R. 2019a
Cited in the paper.
GLUE: A multi-task benchmark and analysis platform for natural language understanding
Wang, A.; Singh, A.; Michael, J.; Hill, F.; Levy, O.; and Bowman, S. R. 2019b
Cited in the paper.
Later among the works it cites.
oLMpics–On what Language Model Pre-training Captures
Talmor, A.; Elazar, Y.; Goldberg, Y.; and Berant, J. 2020 · 2020
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Do Language Embeddings capture Scales?
Zhang, X.; Ramachandran, D.; Tenney, I.; Elazar, Y.; and Roth, D. 2020 · 2020
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Representing Numbers in NLP: a Survey and a Vision
Thawani, A.; Pujara, J.; Szekely, P. A.; and Ilievski, F. 2021 · 2021
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