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Authorship style transfer involves altering text to match the style of a target author whilst preserving the original meaning.
Language Models are Few-Shot Learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 1901
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Roberta: A robustly optimized bert pretraining approach
Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; Levy, O.; Lewis, M.; Zettlemoyer, L.; and Stoyanov, V. 2019 · 1907
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The measurement of observer agreement for categorical data
Landis, J. R.; and Koch, G. G. 1977 · 1977
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Re-evaluating the role of BLEU in machine translation research
Callison-Burch, C.; Osborne, M.; and Koehn, P. 2006 · 2006
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Paraphrasing for style
Xu, W.; Ritter, A.; Dolan, W. B.; Grishman, R.; and Cherry, C. 2012 · 2012
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Evaluating prose style transfer with the Bible
Carlson, K.; Riddell, A.; and Rockmore, D. 2018 · 2018
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Cer, D.; Yang, Y.; Kong, S.-y.; Hua, N.; Limtiaco, N.; John, R. S.; Constant, N.; Guajardo-Cespedes, M.; Yuan, S.; Tar, C.; et al. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Multiple-attribute text rewriting
Lample, G.; Subramanian, S.; Smith, E.; Denoyer, L.; Ranzato, M.; and Boureau, Y.-L. 2018 · 2018
Earlier work this paper cites.
Delete, retrieve, generate: a simple approach to sentiment and style transfer
Li, J.; Jia, R.; He, H.; and Liang, P. 2018 · 2018
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Multiple-Attribute Text Rewriting
Lample, G.; Subramanian, S.; Smith, E. M.; Denoyer, L.; Ranzato, M.; and Boureau, Y.-L. 2019 · 2019
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; and Sutskever, I. 2019 · 2019
Cited alongside, same era.
“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer
Sudhakar, A.; Upadhyay, B.; and Maheswaran, A. 2019 · 2019
Cited alongside, same era.
Neural network acceptability judgments
Warstadt, A.; Singh, A.; and Bowman, S. R. 2019 · 2019
Cited alongside, same era.
Reformulating Unsupervised Style Transfer as Paraphrase Generation
Krishna, K.; Wieting, J.; and Iyyer, M. 2020 · 2020
Cited alongside, same era.
On Variational Learning of Controllable Representations for Text without Supervision
Xu, P.; Cheung, J. C. K.; and Cao, Y. 2020 · 2020
Cited alongside, same era.
TopGuNN: Fast NLP Training Data Augmentation using Large Corpora
Iglesias-Flores, R.; Mishra, M.; Patel, A.; Malhotra, A.; Kriz, R.; Palmer, M.; and Callison-Burch, C. 2021 · 2021
A large-scale computational study of content preservation measures for text style transfer and paraphrase generation
Babakov, N.; Dale, D.; Logacheva, V.; and Panchenko, A. 2022 · 2022
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BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model
BigScience. 2022 · 2022
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Scaling Instruction-Finetuned Language Models
Chung, H. W.; Hou, L.; Longpre, S.; Zoph, B.; Tay, Y.; Fedus, W.; Li, E.; Wang, X.; Dehghani, M.; Brahma, S.; et al. 2022 · 2022
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Survey of Low-Resource Machine Translation
Haddow, B.; Bawden, R.; Miceli Barone, A. V.; Helcl, J.; and Birch, A. 2022 · 2022
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Deep Learning for Text Style Transfer: A Survey
Jin, D.; Jin, Z.; Hu, Z.; Vechtomova, O.; and Mihalcea, R. 2022 · 2022
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Cited alongside, same era.
Billion-Scale Similarity Search with GPUs
Johnson, J.; Douze, M.; and Jégou, H. 2021 · 2021
Cited alongside, same era.
A deep metric learning approach to account linking
Khan, A.; Fleming, E.; Schofield, N.; Bishop, M.; and Andrews, N. 2021 · 2021
Cited alongside, same era.
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
Riley, P.; Constant, N.; Guo, M.; Kumar, G.; Uthus, D. C.; and Parekh, Z. 2021 · 2021
Cited alongside, same era.
Learning Universal Authorship Representations
Rivera-Soto, R. A.; Miano, O. E.; Ordonez, J.; Chen, B. Y.; Khan, A.; Bishop, M.; and Andrews, N. 2021 · 2021
Cited alongside, same era.
GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model
Wang, B.; and Komatsuzaki, A. 2021 · 2021
Cited alongside, same era.
Patel, A.; Li, B.; Rasooli, M. S.; Constant, N.; Raffel, C.; and Callison-Burch, C. 2022 · 2022
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Measuring and Narrowing the Compositionality Gap in Language Models
Press, O.; Zhang, M.; Min, S.; Schmidt, L.; Smith, N. A.; and Lewis, M. 2022 · 2022
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A Recipe for Arbitrary Text Style Transfer with Large Language Models
Reif, E.; Ippolito, D.; Yuan, A.; Coenen, A.; Callison-Burch, C.; and Wei, J. 2022 · 2022
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Same Author or Just Same Topic? Towards Content-Independent Style Representations
Wegmann, A.; Schraagen, M.; and Nguyen, D. 2022 · 2022
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Chain of thought prompting elicits reasoning in large language models
Wei, J.; Wang, X.; Schuurmans, D.; Bosma, M.; Chi, E.; Le, Q.; and Zhou, D. 2022 · 2022
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Opt: Open pre-trained transformer language models
Zhang, S.; Roller, S.; Goyal, N.; Artetxe, M.; Chen, M.; Chen, S.; Dewan, C.; Diab, M.; Li, X.; Lin, X. V.; et al. 2022 · 2022
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