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In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language models for downstream tasks.
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 · 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, et al. 2019 · 1910
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Unsupervised word sense disambiguation rivaling supervised methods
David Yarowsky. 1995 · 1995
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Meta fine-tuning neural language models for multi-domain text mining
Chengyu Wang, Minghui Qiu, Jun Huang, and Xiaofeng He. 2020 · 2003
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Detecting change in data streams
Daniel Kifer, Shai Ben-David, and Johannes Gehrke. 2004 · 2004
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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 · 2005
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Neural unsupervised domain adaptation in nlp—a survey
Alan Ramponi and Barbara Plank. 2020 · 2005
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Tri-training: Exploiting unlabeled data using three classifiers
Zhi-Hua Zhou and Ming Li. 2005 · 2005
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Domain adaptation with structural correspondence learning
John Blitzer, Ryan McDonald, 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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Semisupervised learning for computational linguistics
Steven Abney. 2007 · 2007
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Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira. 2007 · 2007
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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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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
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Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, and Lidong Bing. 2020 · 2009
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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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Simple semi-supervised training of part-of-speech taggers
Anders Søgaard. 2010 · 2010
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky. 2015 · 2015
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Domain separation networks
Konstantinos Bousmalis, George Trigeorgis, Nathan Silberman, Dilip Krishnan, and Dumitru Erhan. 2016 · 2016
Cited alongside, same era.
Google’s neural machine translation system: Bridging the gap between human and machine translation
Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. 2016 · 2016
Cited alongside, same era.
Asymmetric tri-training for unsupervised domain adaptation
Kuniaki Saito, Yoshitaka Ushiku, and Tatsuya Harada. 2017 · 2017
Cited alongside, same era.
Adversarial training for cross-domain Universal Dependency parsing
Motoki Sato, Hitoshi Manabe, Hiroshi Noji, and Yuji Matsumoto. 2017 · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Cited alongside, same era.
Unsupervised domain adaptation of contextualized embeddings for sequence labeling
Xiaochuang Han and Jacob Eisenstein. 2019 · 2019
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Cross-domain NER using cross-domain language modeling
Chen Jia, Xiaobo Liang, and Yue Zhang. 2019 · 2019
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Simplified neural unsupervised domain adaptation
Timothy Miller. 2019 · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Deep contextualized self-training for low resource dependency parsing
Guy Rotman and Roi Reichart. 2019 · 2019
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Neural structural correspondence learning for domain adaptation
Yftah Ziser and Roi Reichart. 2017 · 2017
Cited alongside, same era.
Domain adaptation with adversarial training and graph embeddings
Firoj Alam, Shafiq Joty, and Muhammad Imran. 2018 · 2018
Cited alongside, same era.
Multi-source domain adaptation with mixture of experts
Jiang Guo, Darsh Shah, and Regina Barzilay. 2018 · 2018
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder. 2018 · 2018
Cited alongside, same era.
What’s in a domain? learning domain-robust text representations using adversarial training
Yitong Li, Timothy Baldwin, and Trevor Cohn. 2018a · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Strong baselines for neural semi-supervised learning under domain shift
Sebastian Ruder and Barbara Plank. 2018 · 2018
Cited alongside, same era.
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019 · 2019
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BERT post-training for review reading comprehension and aspect-based sentiment analysis
Hu Xu, Bing Liu, Lei Shu, and Philip Yu. 2019 · 2019
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Xlnet: Generalized autoregressive pretraining for language understanding
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019 · 2019
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Task refinement learning for improved accuracy and stability of unsupervised domain adaptation
Yftah Ziser and Roi Reichart. 2019 · 2019
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Perl: Pivot-based domain adaptation for pre-trained deep contextualized embedding models
Eyal Ben-David, Carmel Rabinovitz, and Roi Reichart. 2020 · 2020
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Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov. 2020 · 2020
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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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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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Biobert: a pre-trained biomedical language representation model for biomedical text mining
J Lee, W Yoon, S Kim, D Kim, S Kim, CH So, and J Kang. 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 Carbonell, and Thierry Poibeau. 2020 · 2020
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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
Closest in time.
An embarrassingly simple approach for transfer learning from pretrained language models
Alexandra Chronopoulou, Christos Baziotis, and Alexandros Potamianos. 2019 · 2095
Closest in time.