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Multi-Task Learning (MTL) networks have emerged as a promising method for transferring learned knowledge across different tasks.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao · 1901
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Parameter-efficient transfer learning for NLP
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin de Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly · 1902
Earlier work this paper cites.
Linguistic knowledge and transferability of contextual representations
Nelson F. Liu, Matt Gardner, Yonatan Belinkov, Matthew E. Peters, and Noah A. Smith · 1903
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Which tasks should be learned together in multi-task learning?
Trevor Standley, Amir Roshan Zamir, Dawn Chen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 1905
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BERT rediscovers the classical NLP pipeline
Ian Tenney, Dipanjan Das, and Ellie Pavlick · 1905
Earlier work this paper cites.
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 · 1905
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Superglue: A stickier benchmark for general-purpose language understanding systems
Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R. Bowman · 1905
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Bam! born-again multi-task networks for natural language understanding
Kevin Clark, Minh-Thang Luong, Urvashi Khandelwal, Christopher D. Manning, and Quoc V. Le · 1907
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Task selection policies for multitask learning
John Glover and Chris Hokamp · 1907
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Spanbert: Improving pre-training by representing and predicting spans
Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy · 1907
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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 · 1907
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Huggingface’s 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, and Jamie Brew · 1910
Earlier work this paper cites.
Multitask learning: A knowledge-based source of inductive bias
Richard Caruana · 1993
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Multitask learning
Rich Caruana · 1997
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SemEval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel Cer, Mona Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia · 2001
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett · 2005
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An empirical study of the behavior of active learning for word sense disambiguation
Jinying Chen, Andrew Schein, Lyle Ungar, and Martha Palmer · 2006
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A unified architecture for natural language processing: deep neural networks with multitask learning
Ronan Collobert and Jason Weston · 2008
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Multi-task active learning for linguistic annotations
Roi Reichart, Katrin Tomanek, Udo Hahn, and Ari Rappoport · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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The winograd schema challenge
Hector J. Levesque · 2011
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SemEval-2012 task 7: Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Andrew Gordon, Zornitsa Kozareva, and Melissa Roemmele · 2012
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Counterfactual reasoning and learning systems: The example of computational advertising
Denis Charles, Max Chickering, and Patrice Simard · 2013
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Stochastic variational inference
Matthew D. Hoffman, David M. Blei, Chong Wang, and John Paisley · 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
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A large annotated corpus for learning natural language inference
Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks
Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich · 2017
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Modulating early visual processing by language
Harm de Vries, Florian Strub, Jeremie Mary, Hugo Larochelle, Olivier Pietquin, and Aaron C Courville · 2017
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Results of the WNUT2017 shared task on novel and emerging entity recognition
Leon Derczynski, Eric Nichols, Marieke van Erp, and Nut Limsopatham · 2017
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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
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel Bowman · 2018
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HotpotQA: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D. Manning · 2018
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Record: Bridging the gap between human and machine commonsense reading comprehension
Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme · 2018
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Matthew Dunn, Levent Sagun, Mike Higgins, V. Ugur Güney, Volkan Cirik, and Kyunghyun Cho · 2017
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TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel Weld, and Luke Zettlemoyer · 2017
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder · 2017
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NewsQA: A machine comprehension dataset
Adam Trischler, Tong Wang, Xingdi Yuan, Justin Harris, Alessandro Sordoni, Philip Bachman, and Kaheer Suleman · 2017
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Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
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Massively multilingual neural machine translation
Roee Aharoni, Melvin Johnson, and Orhan Firat · 2019
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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What does BERT look at? an analysis of BERT’s attention
Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning · 2019
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The commitmentbank: Investigating projection in naturally occurring discourse
Marie-Catherine de Marneffe, Mandy Simons, and Judith Tonhauser · 2019
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MRQA 2019 shared task: Evaluating generalization in reading comprehension
Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, and Danqi Chen · 2019
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A hybrid neural network model for commonsense reasoning
Pengcheng He, Xiaodong Liu, Weizhu Chen, and Jianfeng Gao · 2019
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Matthew Kelcey, Jacob Devlin, Kenton Lee, Kristina N. Toutanova, Llion Jones, Ming-Wei Chang, Andrew Dai, Jakob Uszkoreit, Quoc Le, and Slav Petrov · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer, 2019
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu · 2019
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BERT and PALs: Projected attention layers for efficient adaptation in multi-task learning
Asa Cooper Stickland, Iain Murray, someone, and someone · 2019
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Language models are few-shot learners
Tom B Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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