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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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Adapter-TST: A Parameter Efficient Method for Multiple-Attribute Text Style Transfer
Hu, Z., Chen, N. F., & Lee, R. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.50 · Cited by 1
Text Style Transfer Using Partly-Shared Decoder
Halevi, G., & Wadhawan, K. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00225 · Cited by 1
Text Sentiment Transfer Methods by Using Sentence Keywords
Hu, S., Li, B., Lin, K., Wang, R., & Liu, K. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-55789-8_4 · Cited by 1
NLP Service APIs and Models for Efficient Registration of New Clients
Shah, S., Piratla, V., Chakrabarti, S., & Sarawagi, S. (2020) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2020.findings-emnlp.357 · Cited by 1
Large Scale Author Obfuscation Using Siamese Variational Auto-Encoder: The SiamAO System.
Saedi, C., & Dras, M. (2020) · Joint Conference on Lexical and Computational Semantics · Cited by 1
Kagawa, R., Baba, Y., & Tsurushima, H. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671418 · Cited by 1
Generating Content-Preserving and Semantics-Flipping Adversarial Text
Pei, W., & Yue, C. (2022) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3488932.3517397 · Cited by 2
Controlled text style transfer via noise enhancement of deep learning transformer
Zhang, X., Shi, S., Guo, Z., Chen, G., Wei, H., Tang, Y., & Yu, L. (2022) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1117/12.2639492 · Cited by 1
Quick Estimate of Information Decomposition for Text Style Transfer
Shibaev, V., Olbrich, E., Jost, J., & Yamshchikov, I. P. (2023) · Entropy · doi:10.3390/e25020322 · Cited by 1
T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation
Jangra, A., Nema, P., & Raghuveer, A. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.602 · Cited by 2
Incorporating Pseudo-Parallel Data for Quantifiable Sequence Editing.Preprint
Liao, Y., Bing, L., Li, P., Shi, S., Lam, W. P., & Zhang, T. (2018) · arXiv (Cornell University) · Cited by 1
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.
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