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The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks.
EQUATE: A benchmark evaluation framework for quantitative reasoning in natural language inference
Abhilasha Ravichander, Aakanksha Naik, Carolyn Rose, and Eduard Hovy. 2019 · 1901
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On random weights and unsupervised feature learning
Andrew M Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y Ng. 2011 · 2011
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Alex Graves, Greg Wayne, and Ivo Danihelka. 2014 · 2014
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GloVe: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014 · 2014
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Assessing the ability of LSTMs to learn syntax-sensitive dependencies
Tal Linzen, Emmanuel Dupoux, and Yoav Goldberg. 2016 · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016 · 2016
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Order-embeddings of images and language
Ivan Vendrov, Ryan Kiros, Sanja Fidler, and Raquel Urtasun. 2016 · 2016
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Order matters: Sequence to sequence for sets
Oriol Vinyals, Samy Bengio, and Manjunath Kudlur. 2016 · 2016
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Finegrained analysis of sentence embeddings using auxiliary prediction tasks
Yossi Adi, Einat Kermany, Yonatan Belinkov, Ofer Lavi, and Yoav Goldberg. 2017 · 2017
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Verb physics: Relative physical knowledge of actions and objects
Maxwell Forbes and Yejin Choi. 2017 · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang. 2017 · 2017
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Neural semantic parsing with type constraints for semi-structured tables
Jayant Krishnamurthy, Pradeep Dasigi, and Matt Gardner. 2017 · 2017
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Bidirectional attention flow for machine comprehension
Min Joon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2017 · 2017
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What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, and Marco Baroni. 2018 · 2018
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Sharp nearby, fuzzy far away: How neural language models use context
Urvashi Khandelwal, He He, Peng Qi, and Dan Jurafsky. 2018 · 2018
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Advances in pre-training distributed word representations
Extracting commonsense properties from embeddings with limited human guidance
Yiben Yang, Larry Birnbaum, Ji-Ping Wang, and Doug Downey. 2018 · 2018
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QANet: Combining local convolution with global self-attention for reading comprehension
Adams Wei Yu, David Dohan, Minh-Thang Luong, Rui Zhao, Kai Chen, Mohammad Norouzi, and Quoc V. Le. 2018 · 2018
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Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel. 2019 · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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DROP: A reading comprehension benchmark requiring discrete reasoning over paragraphs
Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, and Matt Gardner. 2019 · 2019
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Tomas Mikolov, Edouard Grave, Piotr Bojanowski, Christian Puhrsch, and Armand Joulin. 2018 · 2018
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Deep contextualized word representations
Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Cited alongside, same era.
Numeracy for language models: Evaluating and improving their ability to predict numbers
Georgios Spithourakis and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
Neural arithmetic logic units
Andrew Trask, Felix Hill, Scott Reed, Jack W. Rae, Chris Dyer, and Phil Blunsom. 2018 · 2018
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On the practical computational power of finite precision rnns for language recognition
Gail Weiss, Yoav Goldberg, and Eran Yahav. 2018 · 2018
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Learning numerical attributes in knowledge bases
Bhushan Kotnis and Alberto García-Durán. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F Liu, Matt Gardner, Yonatan Belinkov, Matthew Peters, and Noah A Smith. 2019 · 2019
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Exploring numeracy in word embeddings
Aakanksha Naik, Abhilasha Ravichander, Carolyn Rose, and Eduard Hovy. 2019 · 2019
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Analysing mathematical reasoning abilities of neural models
David Saxton, Edward Grefenstette, Felix Hill, and Pushmeet Kohli. 2019 · 2019
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