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Textual style transfer is the task of transforming stylistic properties of text while preserving meaning.
The Curious Case of Neural Text Degeneration
Holtzman, A.; Buys, J.; Du, L.; Forbes, M.; and Choi, Y. 2020 · 1904
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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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Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks
Reimers, N.; and Gurevych, I. 2019 · 1908
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Learning Invariant Representations of Social Media Users
Andrews, N.; and Bishop, M. 2019 · 1910
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Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
Raffel, C.; Shazeer, N.; Roberts, A.; Lee, K.; Narang, S.; Matena, M.; Zhou, Y.; Li, W.; and Liu, P. J. 2020 · 1910
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HuggingFace’s Transformers: State-of-the-art Natural Language Processing
Wolf, T.; Debut, L.; Sanh, V.; Chaumond, J.; Delangue, C.; Moi, A.; Cistac, P.; Rault, T.; Louf, R.; Funtowicz, M.; Davison, J.; Shleifer, S.; von Platen, P.; Ma, C.; Jernite, Y.; Plu, J.; Xu, C.; Scao, T. L.; Gugger, S.; Drame, M.; Lhoest, Q.; and Rush, A. M. 2020 · 1910
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Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S.; Madotto, A.; Lan, J.; Hung, J.; Frank, E.; Molino, P.; Yosinski, J.; and Liu, R. 2020 · 1912
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PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
Zhang, J.; Zhao, Y.; Saleh, M.; and Liu, P. J. 2020 · 1912
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WordNet: A Lexical Database for English
Miller, G. A. 1994 · 1994
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The Enron Corpus: A New Dataset for Email Classification Research
Klimt, B.; and Yang, Y. 2004 · 2004
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; 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. M.; 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 · 2005
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Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2006
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Natural Language Processing with Python
Bird, S.; Klein, E.; and Loper, E. 2009 · 2009
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GeDi: Generative Discriminator Guided Sequence Generation
Krause, B.; Gotmare, A. D.; McCann, B.; Keskar, N. S.; Joty, S.; Socher, R.; and Rajani, N. F. 2020 · 2009
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Reformulating Unsupervised Style Transfer as Paraphrase Generation
Krishna, K.; Wieting, J.; and Iyyer, M. 2020 · 2010
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statsmodels: Econometric and statistical modeling with python
Seabold, S.; and Perktold, J. 2010 · 2010
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Denoising Diffusion Implicit Models
Song, J.; Meng, C.; and Ermon, S. 2022 · 2010
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Email Formality in the Workplace: A Case Study on the Enron Corpus
Peterson, K.; Hohensee, M.; and Xia, F. 2011 · 2011
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Efficient Estimation of Word Representations in Vector Space
Mikolov, T.; Chen, K.; Corrado, G.; and Dean, J. 2013 · 2013
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J.; Weiss, E. A.; Maheswaranathan, N.; and Ganguli, S. 2015 · 2015
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019 · 2019
Cited alongside, same era.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; Desmaison, A.; Kopf, A.; Yang, E.; DeVito, Z.; Raison, M.; Tejani, A.; Chilamkurthy, S.; Steiner, B.; Fang, L.; Bai, J.; and Chintala, S. 2019 · 2019
Cited alongside, same era.
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.
Neural Network Acceptability Judgments
Warstadt, A.; Singh, A.; and Bowman, S. R. 2019 · 2019
Cited alongside, same era.
TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification
Barbieri, F.; Camacho-Collados, J.; Espinosa Anke, L.; and Neves, L. 2020 · 2020
Cited alongside, same era.
FUDGE: Controlled Text Generation With Future Discriminators
Yang, K.; and Klein, D. 2021 · 2021
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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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Accelerate: Training and inference at scale made simple, efficient and adaptable
Gugger, S.; Debut, L.; Wolf, T.; Schmid, P.; Mueller, Z.; Mangrulkar, S.; Sun, M.; and Bossan, B. 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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Diffusion-LM Improves Controllable Text Generation
Li, X. L.; Thickstun, J.; Gulrajani, I.; Liang, P.; and Hashimoto, T. B. 2022 · 2022
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Experiment Tracking with Weights and Biases
Biewald, L. 2020 · 2020
Cited alongside, same era.
Array programming with NumPy
Harris, C. R.; Millman, K. J.; van der Walt, S. J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; Wieser, E.; Taylor, J.; Berg, S.; Smith, N. J.; Kern, R.; Picus, M.; Hoyer, S.; van Kerkwijk, M. H.; Brett, M.; Haldane, A.; del Río, J. F.; Wiebe, M.; Peterson, P.; Gérard-Marchant, P.; Sheppard, K.; Reddy, T.; Weckesser, W.; Abbasi, H.; Gohlke, C.; and Oliphant, T. E. 2020 · 2020
Cited alongside, same era.
PowerTransformer: Unsupervised Controllable Revision for Biased Language Correction
Ma, X.; Sap, M.; Rashkin, H.; and Choi, Y. 2020 · 2020
Cited alongside, same era.
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
Morris, J.; Lifland, E.; Yoo, J. Y.; Grigsby, J.; Jin, D.; and Qi, Y. 2020 · 2020
Cited alongside, same era.
pandas-dev/pandas: Pandas
pandas development team, T. 2020 · 2020
Cited alongside, same era.
PySBD: Pragmatic Sentence Boundary Disambiguation
Sadvilkar, N.; and Neumann, M. 2020 · 2020
Cited alongside, same era.
XFORMAL: A Benchmark for Multilingual Formality Style Transfer
Briakou, E.; Lu, D.; Zhang, K.; and Tetreault, J. 2021 · 2021
Cited alongside, same era.
Mireshghallah, F.; Goyal, K.; and Berg-Kirkpatrick, T. 2022 · 2022
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Low-Resource Authorship Style Transfer with In-Context Learning
Patel, A.; Andrews, N.; and Callison-Burch, C. 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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Palette: Image-to-Image Diffusion Models
Saharia, C.; Chan, W.; Chang, H.; Lee, C. A.; Ho, J.; Salimans, T.; Fleet, D. J.; and Norouzi, M. 2022 · 2022
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Self-conditioned Embedding Diffusion for Text Generation
Strudel, R.; Tallec, C.; Altché, F.; Du, Y.; Ganin, Y.; Mensch, A.; Grathwohl, W.; Savinov, N.; Dieleman, S.; Sifre, L.; and Leblond, R. 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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Likelihood-Based Diffusion Language Models
Gulrajani, I.; and Hashimoto, T. B. 2023 · 2023
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Han, X.; Kumar, S.; and Tsvetkov, Y. 2023 · 2023
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SSD-2: Scaling and Inference-time Fusion of Diffusion Language Models
Han, X.; Kumar, S.; Tsvetkov, Y.; and Ghazvininejad, M. 2023 · 2023
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More than a Feeling: Accuracy and Application of Sentiment Analysis
Hartmann, J.; Heitmann, M.; Siebert, C.; and Schamp, C. 2023 · 2023
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TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Mahabadi, R. K.; Tae, J.; Ivison, H.; Henderson, J.; Beltagy, I.; Peters, M. E.; and Cohan, A. 2023 · 2023
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explosion/spaCy: v3.6.1: Support for Pydantic v2, find-function CLI and more
Montani, I.; Honnibal, M.; Honnibal, M.; Boyd, A.; Landeghem, S. V.; Peters, H.; McCann, P. O.; jim geovedi; O’Regan, J.; Samsonov, M.; de Kok, D.; Orosz, G.; Blättermann, M.; Altinok, D.; Kannan, M.; Mitsch, R.; Kristiansen, S. L.; Edward; Miranda, L.; Baumgartner, P.; Bournhonesque, R.; Hudson, R.; Bot, E.; Roman; Fiedler, L.; Daniels, R.; kadarakos; Phatthiyaphaibun, W.; and Schero1994. 2023 · 2023
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Veselovsky, V.; Ribeiro, M. H.; and West, R. 2023 · 2023
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SeqDiffuSeq: Text Diffusion with Encoder-Decoder Transformers
Yuan, H.; Yuan, Z.; Tan, C.; Huang, F.; and Huang, S. 2023 · 2023
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