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Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing.
Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
Earlier work this paper cites.
Recursive deep models for semantic compositionality over a sentiment treebank
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
Earlier work this paper cites.
SentEval: An evaluation toolkit for universal sentence representations
Alexis Conneau and Douwe Kiela. 2018 · 2018
Earlier work this paper cites.
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
Earlier work this paper cites.
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
Earlier work this paper cites.
What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. 2019 · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Earlier work this paper cites.
A structural probe for finding syntax in word representations
John Hewitt and Christopher D. Manning. 2019 · 2019
Cited alongside, same era.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
Cited alongside, same era.
Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith. 2019 · 2019
Cited alongside, same era.
Neural Network Acceptability Judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2019 · 2019
Cited alongside, same era.
Investigating learning dynamics of BERT fine-tuning
Yaru Hao, Li Dong, Furu Wei, and Ke Xu. 2020 · 2020
Cited alongside, same era.
Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words
Josef Klafka and Allyson Ettinger. 2020 · 2020
What happens to BERT embeddings during fine-tuning?
Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, and Ian Tenney. 2020 · 2020
Later among the works it cites.
Asking without telling: Exploring latent ontologies in contextual representations
Julian Michael, Jan A. Botha, and Ian Tenney. 2020 · 2020
Later among the works it cites.
On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers
Marius Mosbach, Anna Khokhlova, Michael A. Hedderich, and Dietrich Klakow. 2020 · 2020
Later among the works it cites.
Perturbed masking: Parameter-free probing for analyzing and interpreting BERT
Zhiyong Wu, Yun Chen, Ben Kao, and Qun Liu. 2020 · 2020
Later among the works it cites.
How does BERT’s attention change when you fine-tune? an analysis methodology and a case study in negation scope
Yiyun Zhao and Steven Bethard. 2020 · 2020
Later among the works it cites.
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Cited alongside, same era.
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, Samuel R. Bowman, Dipanjan Das, and Ellie Pavlick. 2019b
Cited in the paper.
How transfer learning impacts linguistic knowledge in deep NLP models?
Nadir Durrani, Hassan Sajjad, and Fahim Dalvi. 2021 · 2021
Later among the works it cites.