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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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Pang, R. Y., & Gimpel, K. (2019) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/d19-5614 · Cited by 27
Controlling the Reading Level of Machine Translation Output
Marchisio, K., Guo, J., Lai, C.-I., & Koehn, P. (2019) · Cited by 27
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
Xu, Q., Qu, L., Xu, C., & Cui, R. (2019) · International Conference on Natural Language Generation · doi:10.18653/v1/w19-8633 · Cited by 48
Tran, M., Zhang, Y., & Soleymani, M. (2020) · International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.190 · Cited by 27
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 52
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 57
Self-citationUnsupervised Controllable Text Formalization
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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
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
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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
Shi, K., Wang, Y., Lü, H., Zhu, Y., & Niu, Z. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102564 · Cited by 22
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 41
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
Self-citationUnsupervised Neural Dialect Translation with Commonality and Diversity Modeling
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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
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
Metadata
- 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
- 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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