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Recent work has demonstrated that using parameter efficient tuning techniques such as prefix tuning (or P-tuning) on pretrained language models can yield performance that is comparable or superior to fine-tuning while dramatically reducing trainable parameters.
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Hongyu Guo, Yongyi Mao, and Richong Zhang. 2019a · 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. 2019 · 1905
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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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The PASCAL recognising textual entailment challenge
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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Visualizing Data using t-SNE
Laurens van der Maaten and Geoffrey Hinton. 2008 · 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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Choice of plausible alternatives: An evaluation of commonsense causal reasoning
Melissa Roemmele, Cosmin Adrian Bejan, and Andrew S. Gordon. 2011 · 2011
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Parsing With Compositional Vector Grammars
Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher Manning, Andrew Ng, and Christopher Potts. 2013 · 2013
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Improving Neural Machine Translation Models with Monolingual Data
Rico Sennrich, Barry Haddow, and Alexandra Birch. 2016 · 2016
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A Discriminative Feature Learning Approach for Deep Face Recognition
Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao. 2016 · 2016
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Representation Learning with Contrastive Predictive Coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018 · 2018
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mixup: Beyond Empirical Risk Minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz. 2018 · 2018
Cited alongside, same era.
The CommitmentBank: Investigating projection in naturally occurring discourse
Marie-Catherine De Marneffe, Mandy Simons, and Judith Tonhauser. 2019 · 2019
Cited alongside, same era.
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
Cited alongside, same era.
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 · 2019
Cited alongside, same era.
Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
Cited alongside, same era.
Language Models are Unsupervised Multitask Learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
How Effective is Task-Agnostic Data Augmentation for Pretrained Transformers?
Shayne Longpre, Yu Wang, and Chris DuBois. 2020 · 2020
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AdapterHub: A Framework for Adapting Transformers
Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, Aishwarya Kamath, Ivan Vulić, Sebastian Ruder, Kyunghyun Cho, and Iryna Gurevych. 2020 · 2020
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The tatoeba translation challenge – realistic data sets for low resource and multilingual MT
Jörg Tiedemann. 2020 · 2020
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Big Bird: Transformers for Longer Sequences
Manzil Zaheer, Guru Guruganesh, Kumar Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, and Amr Ahmed. 2020 · 2020
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Deep learning for ai
Yoshua Bengio, Yann Lecun, and Geoffrey Hinton. 2021 · 2021
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A Survey of Data Augmentation Approaches for NLP
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Cited alongside, same era.
Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
Cited alongside, same era.
EDA: Easy data augmentation techniques for boosting performance on text classification tasks
Jason Wei and Kai Zou. 2019 · 2019
Cited alongside, same era.
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, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2019 · 2019
Cited alongside, same era.
Do Not Have Enough Data? Deep Learning to the Rescue!
Ateret Anaby-Tavor, Boaz Carmeli, Esther Goldbraich, Amir Kantor, George Kour, Segev Shlomov, N. Tepper, and Naama Zwerdling. 2020 · 2020
Cited alongside, same era.
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Alexander R. Fabbri, Simeng Han, Haoyuan Li, Haoran Li, Marjan Ghazvininejad, Shafiq Joty, Dragomir Radev, and Yashar Mehdad. 2020 · 2020
Cited alongside, same era.
BAE: BERT-based adversarial examples for text classification
Siddhant Garg and Goutham Ramakrishnan. 2020 · 2020
Cited alongside, same era.
Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, and Eduard H. Hovy. 2021 · 2021
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LoRA: Low-Rank Adaptation of Large Language Models
Edward Hu, Yelong Shen, Phil Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Lu Wang, and Weizhu Chen. 2021 · 2021
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Prefix-Tuning: Optimizing Continuous Prompts for Generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks
Xiao Liu, Kaixuan Ji, Yicheng Fu, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2021 · 2021
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On the stability of fine-tuning {bert}: Misconceptions, explanations, and strong baselines
Marius Mosbach, Maksym Andriushchenko, and Dietrich Klakow. 2021 · 2021
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Learning How to Ask: Querying LMs with Mixtures of Soft Prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Towards a Unified View of Parameter-Efficient Transfer Learning
Junxian He, Chunting Zhou, Xuezhe Ma, Taylor Berg-Kirkpatrick, and Graham Neubig. 2022 · 2022
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On the impact of data augmentation on downstream performance in natural language processing
Itsuki Okimura, Machel Reid, Makoto Kawano, and Yutaka Matsuo. 2022 · 2022
Later among the works it cites.