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The transformer-based pre-trained language models have been tremendously successful in most of the conventional NLP tasks.
Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs
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Swaroop Mishra, Arindam Mitra, Neeraj Varshney, Bhavdeep Sachdeva, and Chitta Baral. 2020 · 2005
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SQuAD: 100,000+ questions for machine comprehension of text
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Exploring the limits of transfer learning with a unified text-to-text transformer
Methods for numeracy-preserving word embeddings
Dhanasekar Sundararaman, Shijing Si, Vivek Subramanian, Guoyin Wang, Devamanyu Hazarika, and Lawrence Carin. 2020 · 2020
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Investigating the limitations of the transformers with simple arithmetic tasks
Rodrigo Nogueira, Zhiying Jiang, and Jimmy Li. 2021 · 2021
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Representing numbers in nlp: a survey and a vision
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Do language embeddings capture scales?
Xikun Zhang, Deepak Ramachandran, Ian Tenney, Yanai Elazar, and Dan Roth. 2020 · 2097
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