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Pre-training large language models has become a standard in the natural language processing community.
Continual learning with tiny episodic memories
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet Kumar Dokania, Philip H. S. Torr, and Marc’Aurelio Ranzato. 2019 · 1902
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Energy and policy considerations for deep learning in NLP
Emma Strubell, Ananya Ganesh, and Andrew McCallum. 2019 · 1906
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Roberta: A robustly optimized BERT pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Principal component analysis
Svante Wold, Kim Esbensen, and Paul Geladi. 1987 · 1987
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen. 1989 · 1989
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Connectionist models of recognition memory: constraints imposed by learning and forgetting functions
Roger Ratcliff. 1990 · 1990
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Child: A first step towards continual learning
Mark B. Ring. 1997 · 1997
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Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition
Erik F. Tjong Kim Sang and Fien De Meulder. 2003 · 2003
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Science from Fisher information: a unification
B Roy Frieden. 2004 · 2004
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Monte carlo computation of the fisher information matrix in nonstandard settings
James C Spall. 2005 · 2005
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The third PASCAL recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and Bill Dolan. 2007 · 2007
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2020 · 2007
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Gaussian Mixture Models , pages 659–663. Springer US, Boston, MA
Douglas Reynolds. 2009 · 2009
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio. 2013 · 2013
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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 Ng, and Christopher Potts. 2013 · 2013
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Aligning books and movies: Towards story-like visual explanations by watching movies and reading books
Yukun Zhu, Ryan Kiros, Rich Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, and Sanja Fidler. 2015 · 2015
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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
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Biocreative v cdr task corpus: a resource for chemical disease relation extraction
Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sciaky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 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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Evaluating layers of representation in neural machine translation on part-of-speech and semantic tagging tasks
Yonatan Belinkov, Lluís Màrquez, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, and James Glass. 2017 · 2017
A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman. 2018 · 2018
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Publicly available clinical BERT embeddings
Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jindi, Tristan Naumann, and Matthew McDermott. 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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Tanda: Transfer and adapt pre-trained transformer models for answer sentence selection
Siddhant Garg, Thuy Vu, and Alessandro Moschitti. 2019 · 2019
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Openwebtext (gokaslan’s distribution, 2019), gpt-2 tokenized
Aaron Gokaslan and Vanya Cohen. 2019 · 2019
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Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017 · 2017
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Overview of the biocreative vi chemical-protein interaction track
Martin Krallinger, Obdulia Rabal, Saber A Akhondi, et al. 2017 · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
S. Henry, Kevin P. Buchan, Michele Filannino, A. Stubbs, and Özlem Uzuner. 2020 · 2018
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R. Bowman. 2018 · 2018
Cited alongside, same era.
What does BERT learn about the structure of language?
Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019 · 2019
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BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 2019
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Episodic memory in lifelong language learning
Cyprien de Masson d’Autume, Sebastian Ruder, Lingpeng Kong, and Dani Yogatama. 2019 · 2019
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Results of the seventh edition of the bioasq challenge
Anastasios Nentidis, Konstantinos Bougiatiotis, Anastasia Krithara, and Georgios Paliouras. 2019 · 2019
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Transfer learning in biomedical natural language processing: An evaluation of BERT and ELMo on ten benchmarking datasets
Yifan Peng, Shankai Yan, and Zhiyong Lu. 2019 · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Unsupervised domain clusters in pretrained language models
Roee Aharoni and Yoav Goldberg. 2020 · 2020
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Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
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Are we pretraining it right? digging deeper into visio-linguistic pretraining
Amanpreet Singh, Vedanuj Goswami, and Devi Parikh. 2020 · 2020
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