Style transfer through back-translation
Open accessAbstract
Style transfer is the task of rephrasing the text to contain specific stylistic properties without changing the intent or affect within the context.This paper introduces a new method for automatic style transfer.We first learn a latent representation of the input sentence which is grounded in a language translation model in order to better preserve the meaning of the sentence while reducing stylistic properties.Then adversarial generation techniques are used to make the output match the desired style.We evaluate this technique on three different style transformations: sentiment, gender and political slant.Compared to two state-of-the-art style transfer modeling techniques we show improvements both in automatic evaluation of style transfer and in manual evaluation of meaning preservation and fluency.
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A large body of work on style transfer is directed at sentence-level sentiment transfer.
“While a large body of work on style transfer is directed at sentence-level sentiment transfer (Li et al., 2018;Prabhumoye et al., 2018)”
From Self-Refine: Iterative Refinement with Self-Feedback (2023) · cites this paper as Prabhumoye et al., 2018
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Liu, D., Fu, J., Zhang, Y., Pal, C., & Lv, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6355 · Cited by 42 · see citing works
Transfer Fine-Tuning: A BERT Case Study
Arase, Y., & Tsujii, J. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1542 · Cited by 41 · see citing works
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer
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Inducing Positive Perspectives with Text Reframing
Ziems, C., Li, M., Zhang, A. L., & Yang, D. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.257 · Cited by 46 · see citing works
Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey
Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.241 · Cited by 38 · see citing works
Self-citationTowards Fine-grained Text Sentiment Transfer
Luo, F., Li, P., Yang, P., Zhou, J., Tan, Y., Chang, B., Sui, Z., & Sun, X. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1194 · Cited by 38 · see citing works
Neural Data-to-Text Generation with LM-based Text Augmentation
Chang, E., Shen, X., Zhu, D., Demberg, V., & Su, H. (2021) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2021.eacl-main.64 · Cited by 38 · see citing works
Substructure Substitution: Structured Data Augmentation for NLP
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Süzgün, M., Melas-Kyriazi, L., & Jurafsky, D. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.141 · Cited by 41 · see citing works
Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning
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Tell Me How to Ask Again: Question Data Augmentation with Controllable Rewriting in Continuous Space
Liu, D., Gong, Y., Fu, J., Yan, Y., Chen, J., Lv, J., Duan, N., & Zhou, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.467 · Cited by 36 · see citing works
Lee, D., Tian, Z., Xue, L., & Zhang, N. L. (2021) · doi:10.18653/v1/2021.acl-long.8 · Cited by 36 · see citing works
Automatic Generation of Socratic Subquestions for Teaching Math Word Problems
Shridhar, K., Mačina, J., El‐Assady, M., Sinha, T., Kapur, M., & Sachan, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.277 · Cited by 36 · see citing works
She Elicits Requirements and He Tests: Software Engineering Gender Bias in Large Language Models
Treude, C., & Hata, H. (2023) · International Conference on Mining Software Repositories (MSR) · doi:10.1109/msr59073.2023.00088 · Cited by 35 · see citing works
Text Analysis in Adversarial Settings
Gröndahl, T., & Asokan, N. (2019) · ACM Computing Surveys · doi:10.1145/3310331 · Cited by 44 · see citing works
Transfer fine-tuning of BERT with phrasal paraphrases
Arase, Y., & Tsujii, J. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101164 · Cited by 36 · see citing works
Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
Lee, J. (2020) · International Conference on Natural Language Generation · doi:10.18653/v1/2020.inlg-1.25 · Cited by 33 · see citing works
A Novel Estimator of Mutual Information for Learning to Disentangle Textual Representations
Colombo, P., Piantanida, P., & Clavel, C. (2021) · Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2021.acl-long.511 · Cited by 35 · see citing works
Style Transfer for Texts: Retrain, Report Errors, Compare with Rewrites
Tikhonov, A., Shibaev, V., Nagaev, A., Nugmanova, A., & Yamshchikov, I. P. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1406 · Cited by 32 · see citing works
Controllable Data Generation by Deep Learning: A Review
Wang, S.-Y., Du, Y., Guo, X., Pan, B., Qin, Z. S., & Zhao, L. (2024) · ACM Computing Surveys · doi:10.1145/3648609 · Cited by 39 · see citing works
QuaSE: Sequence Editing under Quantifiable Guidance
Liao, Y., Bing, L., Li, P., Shi, S., Lam, W. P., & Zhang, T. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1420 · Cited by 31 · see citing works
Unsupervised Stylish Image Description Generation via Domain Layer Norm
Chen, C.-K., Pan, Z., Liu, M.-Y., & Sun, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018151 · Cited by 31 · see citing works
Paraphrase Diversification Using Counterfactual Debiasing
Park, S., Hwang, S.-W., Chen, F., Choo, J., Ha, J.-W., Kim, S., & Yim, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016883 · Cited by 31 · see citing works
Adapting Language Models for Non-Parallel Author-Stylized Rewriting
Syed, B., Verma, G., Srinivasan, B. V., Natarajan, A., & Varma, V. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6433 · Cited by 7 · see citing works
Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer
Briakou, E., Lu, D., Zhang, K., & Tetreault, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.256 · Cited by 32 · see citing works
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
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A Survey of Orthographic Information in Machine Translation
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Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer
Huang, Y., Zhu, W., Xiong, D., Zhang, Y., Hu, C., & Xu, F. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.201 · Cited by 29 · see citing works
On Learning Text Style Transfer with Direct Rewards
Liu, Y., Neubig, G., & Wieting, J. (2021) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2021.naacl-main.337 · Cited by 29 · see citing works
Contextual Text Style Transfer
Cheng, Y., Gan, Z., Zhang, Y., Elachqar, O., Li, D., & Liu, J. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.263 · Cited by 27 · see citing works
Facilitating the Communication of Politeness through Fine-Grained Paraphrasing
Fu, L., Fussell, S. R., & Danescu-Niculescu-Mizil, C. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.416 · Cited by 27 · see citing works
“My Way of Telling a Story”: Persona based Grounded Story Generation
Chandu, K. R., Prabhumoye, S., Salakhutdinov, R., & Black, A. W. (2019) · Workshop on Storytelling (StoryNLP) · doi:10.18653/v1/w19-3402 · Cited by 35 · see citing works
Self-citationUnsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer
Pang, R. Y., & Gimpel, K. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5614 · Cited by 27 · see citing works
Controlling the Reading Level of Machine Translation Output
Marchisio, K., Guo, J., Lai, C.-I., & Koehn, P. (2019) · Cited by 27 · see citing works
Reinforced Rewards Framework for Text Style Transfer
Sancheti, A., Krishna, K., Srinivasan, B. V., & Natarajan, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45439-5_36 · Cited by 27 · see citing works
Xu, Q., Qu, L., Xu, C., & Cui, R. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8633 · Cited by 48 · see citing works
Tran, M., Zhang, Y., & Soleymani, M. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.190 · Cited by 26 · see citing works
LENS: A Learnable Evaluation Metric for Text Simplification
Maddela, M., Dou, Y., Heineman, D., & Xu, W. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.905 · Cited by 38 · see citing works
Gradient-based Constrained Sampling from Language Models
Kumar, S., Paria, B., & Tsvetkov, Y. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.144 · Cited by 28 · see citing works
Self-citationUnsupervised Controllable Text Formalization
Jain, P., Mishra, A., Azad, A. P., & Sankaranarayanan, K. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016554 · Cited by 1 · see citing works
DialTest: automated testing for recurrent-neural-network-driven dialogue systems
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Lai, C.-T., Hong, Y.-T., Chen, H.-Y., Lu, C.-J., & Lin, S.-D. (2019) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d19-1366 · Cited by 25 · see citing works
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
Zheng, Y., Chen, Z., Zhang, R., Huang, S., Mao, X., & Huang, M. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i16.17711 · Cited by 25 · see citing works
Dual Consistency-Enhanced Semi-Supervised Sentiment Analysis Towards COVID-19 Tweets
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FuzzE: Fuzzy Fairness Evaluation of Offensive Language Classifiers on African-American English
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EKGTF: A knowledge-enhanced model for optimizing social network-based meteorological briefings
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Text Style Transfer for Bias Mitigation using Masked Language Modeling
Tokpo, E. K., & Calders, T. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (NAACL SRW) · doi:10.18653/v1/2022.naacl-srw.21 · Cited by 27 · see citing works
Towards Content Transfer through Grounded Text Generation
Prabhumoye, S., Quirk, C., & Galley, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1269 · Cited by 23 · see citing works
Self-citationUnsupervised Neural Dialect Translation with Commonality and Diversity Modeling
Wan, Y. X., Yang, B., Wong, D. F., Chao, L. S., Du, H., & Ao, B. C. H. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6448 · Cited by 1 · see citing works
Transductive Learning for Unsupervised Text Style Transfer
Xiao, F., Pang, L., Lan, Y., Wang, Y., Shen, H., & Cheng, X. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.195 · Cited by 20 · see citing works
Complementary Auxiliary Classifiers for Label-Conditional Text Generation
Li, Y., Li, C., Zhang, Y., Li, X., Zheng, G., Carin, L., & Gao, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6346 · Cited by 19 · see citing works
- Authors
- Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., & Black, A. W.
- Affiliations
- Carnegie Mellon University (US)
- Venue
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2018
- Pages
- 866-876
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W2798651744
- Cited by
- 348
- References
- 41
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 12 · 2023: 41 · 2022: 51 · 2021: 64 · 2020: 71 · 2019: 82 · 2018: 10 · 2017: 1
- FWCI
- 17.11
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Science Foundation (1526745)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- style transfer, back-translation, sentiment transfer, meaning preservation, fluency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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