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Text style transfer is an exciting task within the field of natural language generation that is often plagued by the need for high-quality paired datasets.
The curious case of neural text degeneration
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2019 · 1904
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Evaluating style transfer for text
Remi Mir, Bjarke Felbo, Nick Obradovich, and Iyad Rahwan. 2019 · 1904
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Domain adaptive text style transfer
Dianqi Li, Yizhe Zhang, Zhe Gan, Yu Cheng, Chris Brockett, Ming-Ting Sun, and Bill Dolan. 2019 · 1908
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Neural text generation with unlikelihood training
Sean Welleck, Ilia Kulikov, Stephen Roller, Emily Dinan, Kyunghyun Cho, and Jason Weston. 2019 · 1908
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Beyond bleu: training neural machine translation with semantic similarity
John Wieting, Taylor Berg-Kirkpatrick, Kevin Gimpel, and Graham Neubig. 2019 · 1909
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 1910
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xslue: A benchmark and analysis platform for cross-style language understanding and evaluation
Dongyeop Kang and Eduard Hovy. 2019 · 1911
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Plug and play language models: A simple approach to controlled text generation
Sumanth Dathathri, Andrea Madotto, Janice Lan, Jane Hung, Eric Frank, Piero Molino, Jason Yosinski, and Rosanne Liu. 2019 · 1912
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A probabilistic formulation of unsupervised text style transfer
Junxian He, Xinyi Wang, Graham Neubig, and Taylor Berg-Kirkpatrick. 2020 · 2002
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002 · 2002
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Parallel data augmentation for formality style transfer
Yi Zhang, Tao Ge, and Xu Sun. 2020 · 2005
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Twitter sentiment classification using distant supervision
Alec Go, Richa Bhayani, and Lei Huang. 2009 · 2009
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Multi-style transfer with discriminative feedback on disjoint corpus
Navita Goyal, Balaji Vasan Srinivasan, Anandhavelu Natarajan, and Abhilasha Sancheti. 2020 · 2010
Cited alongside, same era.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2010
Cited alongside, same era.
Norms of valence, arousal, and dominance for 13,915 english lemmas
Amy Beth Warriner, Victor Kuperman, and Marc Brysbaert. 2013 · 2013
Cited alongside, same era.
Diverse beam search: Decoding diverse solutions from neural sequence models
Ashwin K Vijayakumar, Michael Cogswell, Ramprasath R Selvaraju, Qing Sun, Stefan Lee, David Crandall, and Dhruv Batra. 2016 · 2016
Cited alongside, same era.
Toward controlled generation of text
Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P Xing. 2017 · 2017
Multiple-attribute text style transfer
Sandeep Subramanian, Guillaume Lample, Eric Michael Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau. 2018 · 2018
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019 · 2019
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Sentence-bert: Sentence embeddings using siamese bert-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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Automatically neutralizing subjective bias in text
Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi Yang. 2020 · 2020
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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, Peter J Liu, et al. 2020 · 2020
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Cited alongside, same era.
Shakespearizing modern language using copy-enriched sequence-to-sequence models
Harsh Jhamtani, Varun Gangal, Eduard Hovy, and Eric Nyberg. 2017 · 2017
Cited alongside, same era.
Style transfer from non-parallel text by cross-alignment
Tianxiao Shen, Tao Lei, Regina Barzilay, and Tommi Jaakkola. 2017 · 2017
Cited alongside, same era.
Data statements for natural language processing: Toward mitigating system bias and enabling better science
Emily M Bender and Batya Friedman. 2018 · 2018
Cited alongside, same era.
Style transfer in text: Exploration and evaluation
Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018 · 2018
Cited alongside, same era.
Annotation artifacts in natural language inference data
Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel R Bowman, and Noah A Smith. 2018 · 2018
Cited alongside, same era.
Multiple-attribute text rewriting
Guillaume Lample, Sandeep Subramanian, Eric Smith, Ludovic Denoyer, Marc’Aurelio Ranzato, and Y-Lan Boureau. 2018 · 2018
Cited alongside, same era.
Sudha Rao and Joel Tetreault. 2018 · 2018
Cited alongside, same era.
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. 2020 · 2020
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The importance of modeling social factors of language: Theory and practice
Dirk Hovy and Diyi Yang. 2021 · 2021
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Style is not a single variable: Case studies for cross-stylistic language understanding
Dongyeop Kang and Eduard Hovy. 2021 · 2021
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Contextualizing variation in text style transfer datasets
Stephanie Schoch, Wanyu Du, and Yangfeng Ji. 2021 · 2021
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From theories on styles to their transfer in text: Bridging the gap with a hierarchical survey
Enrica Troiano, Aswathy Velutharambath, et al. 2021 · 2021
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Sven Buechel and Udo Hahn. 2022 · 2022
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Deep learning for text style transfer: A survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022 · 2022
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