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Transfer learning (TL) in natural language processing (NLP) has seen a surge of interest in recent years, as pre-trained models have shown an impressive ability to transfer to novel tasks.
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
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Xiaodong Liu, Pengcheng He, Weizhu Chen, and Jianfeng Gao. 2019a · 1904
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Multiqa: An empirical investigation of generalization and transfer in reading comprehension
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Discourse-based evaluation of language understanding
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
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Huggingface’s transformers: State-of-the-art natural language processing
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Fine-tuning pretrained language models: Weight initializations, data orders, and early stopping
Jesse Dodge, Gabriel Ilharco, Roy Schwartz, Ali Farhadi, Hannaneh Hajishirzi, and Noah Smith. 2020 · 2002
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Electra: Pre-training text encoders as discriminators rather than generators
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2003
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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 · 2004
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Automatically constructing a corpus of sentential paraphrases
William B Dolan and Chris Brockett. 2005 · 2005
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An empirical study of multi-task learning on bert for biomedical text mining
Yifan Peng, Qingyu Chen, and Zhiyong Lu. 2020 · 2005
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English intermediate-task training improves zero-shot cross-lingual transfer too
Jason Phang, Phu Mon Htut, Yada Pruksachatkun, Haokun Liu, Clara Vania, Katharina Kann, Iacer Calixto, and Samuel R Bowman. 2020 · 2005
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, C. Vania, K. Kann, and Samuel R. Bowman. 2020a · 2005
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Yada Pruksachatkun, Jason Phang, Haokun Liu, Phu Mon Htut, Xiaoyi Zhang, Richard Yuanzhe Pang, Clara Vania, Katharina Kann, and Samuel R Bowman. 2020b · 2005
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Exploring and predicting transferability across nlp tasks
Tu Vu, Tong Wang, Tsendsuren Munkhdalai, Alessandro Sordoni, Adam Trischler, Andrew Mattarella-Micke, Subhransu Maji, and Mohit Iyyer. 2020 · 2005
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The second PASCAL recognising textual entailment challenge
Roy Bar Haim, Ido Dagan, Bill Dolan, Lisa Ferro, Danilo Giampiccolo, Bernardo Magnini, and Idan Szpektor. 2006 · 2006
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Learning functions to study the benefit of multitask learning
Gabriele Bettgenhäuser, Michael A Hedderich, and Dietrich Klakow. 2020 · 2006
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Identifying beneficial task relations for multi-task learning in deep neural networks
Anders Søgaard and Joachim Bingel. 2017 · 2017
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Multi-task learning for sequence tagging: An empirical study
Soravit Changpinyo, Hexiang Hu, and Fei Sha. 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. 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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Sentence encoders on stilts: Supplementary training on intermediate labeled-data tasks
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Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006 · 2006
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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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Is supervised syntactic parsing beneficial for language understanding? an empirical investigation
Goran Glavas and I. Vulić. 2020 · 2008
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The fifth PASCAL recognizing textual entailment challenge
Luisa Bentivogli, Ido Dagan, Hoa Trang Dang, Danilo Giampiccolo, and Bernardo Magnini. 2009 · 2009
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The Winograd schema challenge
Hector J Levesque, Ernest Davis, and Leora Morgenstern. 2011 · 2011
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Learning from task descriptions
Orion Weller, Nicholas Lourie, Matt Gardner, and Matthew E. Peters. 2020 · 2011
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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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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba. 2014 · 2014
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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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Ilps at trec 2019 conversational assistant track
Nikos Voskarides, Dan Li, A. Panteli, and Pengjie Ren. 2019 · 2019
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Doubletransfer at mediqa 2019: Multi-source transfer learning for natural language understanding in the medical domain
Yichong Xu, X. Liu, C. Li, Hoifung Poon, and Jianfeng Gao. 2019 · 2019
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Dynamic sampling strategies for multi-task reading comprehension
Ananth Gottumukkala, Dheeru Dua, Sameer Singh, and Matt Gardner. 2020 · 2020
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Estimating the influence of auxiliary tasks for multi-task learning of sequence tagging tasks
Fynn Schröder and Chris Biemann. 2020 · 2020
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Sql generation via machine reading comprehension
Zeyu Yan, Jianqiang Ma, Y. Zhang, and Jianping Shen. 2020 · 2020
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Rethinking why intermediate-task fine-tuning works
Ting-Yun Chang and Chi-Jen Lu. 2021 · 2021
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Unicorn on rainbow: A universal commonsense reasoning model on a new multitask benchmark
Nicholas Lourie, Ronan Le Bras, Chandra Bhagavatula, and Yejin Choi. 2021 · 2021
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What to pre-train on? efficient intermediate task selection
Clifton Poth, Jonas Pfeiffer, Andreas Ruckl’e, and Iryna Gurevych. 2021 · 2021
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