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Pre-training models such as BERT have achieved great success in many natural language processing tasks.
Real-time inference in multi-sentence tasks with deep pretrained transformers
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston. 2019 · 1905
Earlier work this paper cites.
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Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
Earlier work this paper cites.
Singular value decomposition and least squares solutions
Gene H Golub and Christian Reinsch. 1971 · 1971
Earlier work this paper cites.
Principal component analysis
Hervé Abdi and Lynne J Williams. 2010 · 2010
Earlier work this paper cites.
Data whitening and random tx mode
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Earlier work this paper cites.
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Eneko Agirre, Daniel Cer, Mona Diab, and Aitor Gonzalez-Agirre. 2012 · 2012
Earlier work this paper cites.
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Faisal Rahutomo, Teruaki Kitasuka, and Masayoshi Aritsugi. 2012 · 2012
Earlier work this paper cites.
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Eneko Agirre, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, and Weiwei Guo. 2013 · 2013
Earlier work this paper cites.
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Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Rada Mihalcea, German Rigau, and Janyce Wiebe. 2014 · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Eneko Agirre, Carmen Banea, Claire Cardie, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Weiwei Guo, Iñigo Lopez-Gazpio, Montse Maritxalar, Rada Mihalcea, German Rigau, Larraitz Uria, and Janyce Wiebe. 2015 · 2015
Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
Later among the works it cites.
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Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Later among the works it cites.
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Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, and Ray Kurzweil. 2018 · 2018
Later among the works it cites.
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Later among the works it cites.
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Later among the works it cites.
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Cited alongside, same era.
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Later among the works it cites.
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