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It can be challenging to train multi-task neural networks that outperform or even match their single-task counterparts.
Multi-task deep neural networks for natural language understanding
Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019b · 1901
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Unifying question answering and text classification via span extraction
Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019 · 1904
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Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 1904
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A simple sequentially rejective multiple test procedure
Sture Holm. 1979 · 1979
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Multitask learning
Rich Caruana. 1997 · 1997
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Automatically constructing a corpus of sentential paraphrases
William B. Dolan and Chris Brockett. 2005 · 2005
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Model compression
Cristian Buciluǎ, Rich Caruana, and Alexandru Niculescu-Mizil. 2006 · 2006
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The third pascal recognizing textual entailment challenge
Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and William B. Dolan. 2007 · 2007
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A unified architecture for natural language processing: deep neural networks with multitask learning
Ronan Collobert and Jason Weston. 2008 · 2008
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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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Do deep nets really need to be deep?
Jimmy Ba and Rich Caruana. 2014 · 2014
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Semi-supervised sequence learning
Andrew M Dai and Quoc V Le. 2015 · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015 · 2015
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Sequence-level knowledge distillation
Yoon Kim and Alexander M. Rush. 2016 · 2016
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Distilling an ensemble of greedy dependency parsers into one mst parser
Adhiguna Kuncoro, Miguel Ballesteros, Lingpeng Kong, Chris Dyer, and Noah A. Smith. 2016 · 2016
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Multi-task sequence to sequence learning
Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, Oriol Vinyals, and Lukasz Kaiser. 2016 · 2016
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Distilling word embeddings: An encoding approach
Lili Mou, Ran Jia, Yan Xu, Ge Li, Lu Zhang, and Zhi Jin. 2016 · 2016
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Actor-mimic: Deep multitask and transfer reinforcement learning
Emilio Parisotto, Jimmy Ba, and Ruslan Salakhutdinov. 2016 · 2016
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Squad: 100, 000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy S. Liang. 2016 · 2016
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Neural machine translation of rare words with subword units
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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Deep multi-task learning with low level tasks supervised at lower layers
Anders Søgaard and Yoav Goldberg. 2016 · 2016
Looking for ELMo’s friends: Sentence-level pretraining beyond language modeling
Samuel R Bowman, Ellie Pavlick, Edouard Grave, Benjamin Van Durme, Alex Wang, Jan Hula, Patrick Xia, Raghavendra Pappagari, R Thomas McCoy, Roma Patel, et al. 2018 · 2018
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Born again neural networks
Tommaso Furlanello, Zachary C Lipton, Michael Tschannen, Laurent Itti, and Anima Anandkumar. 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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The natural language decathlon: Multitask learning as question answering
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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Identifying beneficial task relations for multi-task learning in deep neural networks
Joachim Bingel and Anders Søgaard. 2017 · 2017
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Semeval-2017 task 1: Semantic textual similarity multilingual and crosslingual focused evaluation
Daniel M. Cer, Mona T. Diab, Eneko Agirre, Iñigo Lopez-Gazpio, and Lucia Specia. 2017 · 2017
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A joint many-task model: Growing a neural network for multiple NLP tasks
Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, and Richard Socher. 2017 · 2017
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First quora dataset release: Question pairs
Shankar Iyer, Nikhil Dandekar, and Kornél Csernai. 2017 · 2017
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Adversarial multi-task learning for text classification
Pengfei Liu, Xipeng Qiu, and Xuanjing Huang. 2017 · 2017
Cited alongside, same era.
When is multitask learning effective? Semantic sequence prediction under varying data conditions
Barbara Plank and Héctor Martínez Alonso. 2017 · 2017
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Sentence encoders on STILTs: Supplementary training on intermediate labeled-data tasks
Jason Phang, Thibault Févry, and Samuel R Bowman. 2018 · 2018
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Neural network acceptability judgments
Alex Warstadt, Amanpreet Singh, and Samuel R. Bowman. 2018 · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
Adina Williams, Nikita Nangia, and Samuel R. Bowman. 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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Massive multi-task learning with snorkel metal: Bringing more supervision to bear
Braden Hancock, Clara McCreery, Ines Chami, Vincent Chen, Sen Wu, Jared Dunnmon, Paroma Varma, Max Lam, and Chris Ré. 2019 · 2019
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Latent multi-task architecture learning
Sebastian Ruder, Joachim Bingel, Isabelle Augenstein, and Anders Søgaard. 2019 · 2019
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A hierarchical multi-task approach for learning embeddings from semantic tasks
Victor Sanh, Thomas Wolf, and Sebastian Ruder. 2019 · 2019
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Multilingual neural machine translation with knowledge distillation
Xu Tan, Yi Ren, Di He, Tao Qin, and Tie-Yan Liu. 2019 · 2019
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
Alex Wang, Amapreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel R Bowman. 2019 · 2019
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