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In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, M. Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 1907
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Virtual adversarial training: a regularization method for supervised and semi-supervised learning
Takeru Miyato, Shin-ichi Maeda, Masanori Koyama, and Shin Ishii. 2018 · 1993
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. 2020b · 2003
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Tri-training: Exploiting unlabeled data using three classifiers
Zhi-Hua Zhou and Ming Li. 2005 · 2005
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2006 · 2006
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Reranking and self-training for parser adaptation
David McClosky, Eugene Charniak, and Mark Johnson. 2006 · 2006
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Joint contrastive learning for unsupervised domain adaptation
Changhwa Park, Jonghyun Lee, Jaeyoon Yoo, Minhoe Hur, and Sungroh Yoon. 2020 · 2006
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Biographies, Bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
John Blitzer, Mark Dredze, and Fernando Pereira. 2007 · 2007
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A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010 · 2010
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Cross-domain sentiment classification via spectral feature alignment
Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. 2010 · 2010
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola. 2012 · 2012
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Clear: Contrastive learning for sentence representation
Zhuofeng Wu, Sinong Wang, Jiatao Gu, Madian Khabsa, Fei Sun, and Hao Ma. 2020 · 2012
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Explaining and harnessing adversarial examples
Ian J. Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
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Image-based recommendations on styles and substitutes
Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015 · 2015
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Supervised representation learning: Transfer learning with deep autoencoders
Fuzhen Zhuang, Xiaohu Cheng, Ping Luo, Sinno Jialin Pan, and Qing He. 2015 · 2015
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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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Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada. 2017 · 2017
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Understanding back-translation at scale
Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. 2018 · 2018
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Adaptive semi-supervised learning for cross-domain sentiment classification
Ruidan He, Wee Sun Lee, Hwee Tou Ng, and Daniel Dahlmeier. 2018 · 2018
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Delete, retrieve, generate: a simple approach to sentiment and style transfer
Juncen Li, Robin Jia, He He, and Percy Liang. 2018 · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
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Adversarial domain adaptation for duplicate question detection
Darsh Shah, Tao Lei, Alessandro Moschitti, Salvatore Romeo, and Preslav Nakov. 2018 · 2018
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Wasserstein distance guided representation learning for domain adaptation
Jian Shen, Yanru Qu, Weinan Zhang, and Yong Yu. 2018 · 2018
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Neural structural correspondence learning for domain adaptation
Yftah Ziser and Roi Reichart. 2017 · 2018
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BERT: Pre-training of deep bidirectional transformers for language understanding
Adversarial and domain-aware BERT for cross-domain sentiment analysis
Chunning Du, Haifeng Sun, Jingyu Wang, Qi Qi, and Jianxin Liao. 2020 · 2020
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Bootstrap your own latent - A new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Ávila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko. 2020 · 2020
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Multi-source domain adaptation for text classification via distancenet-bandits
Han Guo, Ramakanth Pasunuru, and Mohit Bansal. 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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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross B. Girshick. 2020 · 2020
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Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019 · 2019
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Unsupervised domain adaptation of contextualized embeddings for sequence labeling
Xiaochuang Han and Jacob Eisenstein. 2019 · 2019
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Drop to adapt: Learning discriminative features for unsupervised domain adaptation
Seungmin Lee, Dongwan Kim, Namil Kim, and Seong-Gyun Jeong. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Domain adaptation with BERT-based domain classification and data selection
Xiaofei Ma, Peng Xu, Zhiguo Wang, Ramesh Nallapati, and Bing Xiang. 2019 · 2019
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Simplified neural unsupervised domain adaptation
Timothy Miller. 2019 · 2019
Cited alongside, same era.
fairseq: A fast, extensible toolkit for sequence modeling
Myle Ott, Sergey Edunov, Alexei Baevski, Angela Fan, Sam Gross, Nathan Ng, David Grangier, and Michael Auli. 2019 · 2019
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SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization
Haoming Jiang, Pengcheng He, Weizhu Chen, Xiaodong Liu, Jianfeng Gao, and Tuo Zhao. 2020 · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020 · 2020
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Semi-supervised learning on meta structure: Multi-task tagging and parsing in low-resource scenarios
KyungTae Lim, Jay Yoon Lee, Jaime G. Carbonell, and Thierry Poibeau. 2020 · 2020
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Neural unsupervised domain adaptation in NLP—A survey
Alan Ramponi and Barbara Plank. 2020 · 2020
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Feature adaptation of pre-trained language models across languages and domains with robust self-training
Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, and Lidong Bing. 2020 · 2020
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Freelb: Enhanced adversarial training for natural language understanding
Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Tom Goldstein, and Jingjing Liu. 2020 · 2020
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SimCSE: Simple contrastive learning of sentence embeddings
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021 · 2021
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DeCLUTR: Deep contrastive learning for unsupervised textual representations
John Giorgi, Osvald Nitski, Bo Wang, and Gary Bader. 2021 · 2021
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UDALM: Unsupervised domain adaptation through language modeling
Constantinos Karouzos, Georgios Paraskevopoulos, and Alexandros Potamianos. 2021 · 2021
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Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Jörn-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Rémi Le Priol, and Aaron C. Courville. 2021 · 2021
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Targeted adversarial training for natural language understanding
Lis Pereira, Xiaodong Liu, Hao Cheng, Hoifung Poon, Jianfeng Gao, and Ichiro Kobayashi. 2021 · 2021
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Coda: Contrast-enhanced and diversity-promoting data augmentation for natural language understanding
Yanru Qu, Dinghan Shen, Yelong Shen, Sandra Sajeev, Weizhu Chen, and Jiawei Han. 2021 · 2021
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Cross-domain contrastive learning for unsupervised domain adaptation
Rui Wang, Zuxuan Wu, Zejia Weng, Jingjing Chen, Guo-Jun Qi, and Yu-Gang Jiang. 2021 · 2021
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ConSERT: A contrastive framework for self-supervised sentence representation transfer
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021 · 2021
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky. 2016 · 2030
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