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How does language model pretraining help transfer learning? We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance.
Assessing BERT’s syntactic abilities
Yoav Goldberg. 2019 · 1901
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. 2019 · 1910
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Comparison of the predicted and observed secondary structure of t4 phage lysozyme
Brian W Matthews. 1975 · 1975
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. 2013 · 2013
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How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio, and Hod Lipson. 2014 · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio. 2016 · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. 2016 · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 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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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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Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
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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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Rethinking imagenet pre-training
Kaiming He, Ross Girshick, and Piotr Dollár. 2019 · 2019
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A structural probe for finding syntax in word representations
John Hewitt and Christopher D Manning. 2019 · 2019
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Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019 · 2019
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PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
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Language models are unsupervised multitask learners
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Dieuwke Hupkes, Sara Veldhoen, and Willem Zuidema. 2018 · 2018
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Deep contextualized word representations
Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018 · 2018
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BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick. 2019a
Cited in the paper.
What do you learn from context? probing for sentence structure in contextualized word representations
Ian Tenney, Patrick Xia, Berlin Chen, Alex Wang, Adam Poliak, R Thomas McCoy, Najoung Kim, Benjamin Van Durme, Sam Bowman, Dipanjan Das, and Ellie Pavlick. 2019b
Cited in the paper.
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Transfusion: Understanding transfer learning for medical imaging
Maithra Raghu, Chiyuan Zhang, Jon Kleinberg, and Samy Bengio. 2019 · 2019
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R Bowman. 2019 · 2019
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