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Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples.
CTRL: A conditional transformer language model for controllable generation
Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019 · 1909
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Introducing mantis: a novel multi-domain information seeking dialogues dataset
Gustavo Penha, Alexandru Balan, and Claudia Hauff. 2019 · 1912
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Supervised and unsupervised PCFG adaptation to novel domains
Brian Roark and Michiel Bacchiani. 2003 · 2003
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Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard S. Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann. 2020 · 2004
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Domain adaptation with structural correspondence learning
John Blitzer, Ryan T. McDonald, and Fernando Pereira. 2006 · 2006
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Domain adaptation for statistical classifiers
Hal Daumé III and Daniel Marcu. 2006 · 2006
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Correcting sample selection bias by unlabeled data
Jiayuan Huang, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, and Bernhard Schölkopf. 2006 · 2006
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Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification
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Self-training for enhancement and domain adaptation of statistical parsers trained on small datasets
Roi Reichart and Ari Rappoport. 2007 · 2007
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Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. 2010 · 2010
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Xavier Glorot, Antoine Bordes, and Yoshua Bengio. 2011 · 2011
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Minmin Chen, Zhixiang Eddie Xu, Kilian Q. Weinberger, and Fei Sha. 2012 · 2012
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Improved parsing and pos tagging using inter-sentence consistency constraints
Alexander M Rush, Roi Reichart, Michael Collins, and Amir Globerson. 2012 · 2012
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Fast easy unsupervised domain adaptation with marginalized structured dropout
Yi Yang and Jacob Eisenstein. 2014 · 2014
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The airline review dataset
Quang Nguyen. 2015 · 2015
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Character-level convolutional networks for text classification
Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015 · 2015
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Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, Andrew M. Dai, Rafal Józefowicz, and Samy Bengio. 2016 · 2016
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Domain-adversarial training of neural networks
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A persona-based neural conversation model
Jiwei Li, Michel Galley, Chris Brockett, Georgios P. Spithourakis, Jianfeng Gao, and William B. Dolan. 2016 · 2016
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Semeval-2016 task 5: Aspect based sentiment analysis
Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad Al-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphée De Clercq, Véronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia V. Loukachevitch, Evgeniy V. Kotelnikov, Núria Bel, Salud María Jiménez Zafra, and Gülsen Eryigit. 2016 · 2016
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Neural structural correspondence learning for domain adaptation
Yftah Ziser and Roi Reichart. 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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Contextual augmentation: Data augmentation by words with paradigmatic relations
Sosuke Kobayashi. 2018 · 2018
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Deep pivot-based modeling for cross-language cross-domain transfer with minimal guidance
Yftah Ziser and Roi Reichart. 2018a · 2018
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Pivot based language modeling for improved neural domain adaptation
Yftah Ziser and Roi Reichart. 2018b · 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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Keep calm and switch on! preserving sentiment and fluency in semantic text exchange
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020 · 2020
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Control, generate, augment: A scalable framework for multi-attribute text generation
Giuseppe Russo, Nora Hollenstein, Claudiu Cristian Musat, and Ce Zhang. 2020 · 2020
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Cat-gen: Improving robustness in NLP models via controlled adversarial text generation
Tianlu Wang, Xuezhi Wang, Yao Qin, Ben Packer, Kang Li, Jilin Chen, Alex Beutel, and Ed Chi. 2020 · 2020
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Identifying spurious correlations for robust text classification
Zhao Wang and Aron Culotta. 2020 · 2020
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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. 2020 · 2020
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Steven Y. Feng, Aaron W. Li, and Jesse Hoey. 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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Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation
Ashutosh Kumar, Satwik Bhattamishra, Manik Bhandari, and Partha P. Talukdar. 2019 · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Nils Reimers and Iryna Gurevych. 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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EDA: easy data augmentation techniques for boosting performance on text classification tasks
Jason W. Wei and Kai Zou. 2019 · 2019
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Transformer based multi-source domain adaptation
Dustin Wright and Isabelle Augenstein. 2020 · 2020
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Unsupervised data augmentation for consistency training
Qizhe Xie, Zihang Dai, Eduard H. Hovy, Thang Luong, and Quoc Le. 2020 · 2020
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PADA: A prompt-based autoregressive approach for adaptation to unseen domains
Eyal Ben-David, Nadav Oved, and Roi Reichart. 2021 · 2021
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Jiaao Chen, Derek Tam, Colin Raffel, Mohit Bansal, and Diyi Yang. 2021 · 2021
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A survey of data augmentation approaches for NLP
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An investigation of the (in)effectiveness of counterfactually augmented data
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Entony Lekhtman, Yftah Ziser, and Roi Reichart. 2021 · 2021
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Are VQA systems rad? measuring robustness to augmented data with focused interventions
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How does counterfactually augmented data impact models for social computing constructs?
Indira Sen, Mattia Samory, Fabian Flöck, Claudia Wagner, and Isabelle Augenstein. 2021 · 2021
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
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Towards zero-label language learning
Zirui Wang, Adams Wei Yu, Orhan Firat, and Yuan Cao. 2021 · 2021
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Polyjuice: Generating counterfactuals for explaining, evaluating, and improving models
Tongshuang Wu, Marco Túlio Ribeiro, Jeffrey Heer, and Daniel S. Weld. 2021 · 2021
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Cross-domain review generation for aspect-based sentiment analysis
Jianfei Yu, Chenggong Gong, and Rui Xia. 2021 · 2021
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Towards generating long and coherent text with multi-level latent variable models
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