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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. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.03747 · Cited by 8
Automatic Dialect Adaptation in Finnish and its Effect on Perceived CreativityPreprint
Hämäläinen, M. K., Partanen, N., Alnajjar, K., Rueter, J., & Poibeau, T. (2020) · Preprints.org · doi:10.20944/preprints202009.0172.v1 · Cited by 7
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence
Lin, K., Liu, M.-Y., Sun, M., & Kautz, J. (2020) · Workshop on Neural Generation and Translation (WNGT) · doi:10.18653/v1/2020.ngt-1.2 · Cited by 7
Changing the narrative perspective: From deictic to anaphoric point of view
Chen, M., & Bunescu, R. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102559 · Cited by 7
Reinforced Sequence Training based Subjective Bias Correction
Madanagopal, K., & Caverlee, J. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.189 · Cited by 6
Monolingual Transfer Learning via Bilingual Translators for Style-Sensitive Paraphrase Generation
Kajiwara, T., Miura, B., & Arase, Y. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i05.6314 · Cited by 6
Machine-Aided Annotation for Fine-Grained Proposition Types in Argumentation
Jo, Y., Mayfield, E., Reed, C., & Hovy, E. H. (2020) · Discovery Research Portal (University of Dundee) · Cited by 6
Leveraging Low-resource Parallel Data for Text Style Transfer
Mukherjee, S., & Dušek, O. (2023) · International Conference on Natural Language Generation · doi:10.18653/v1/2023.inlg-main.27 · Cited by 7
Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
Helbig, D., Troiano, E., & Klinger, R. (2020) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2020.socialnlp-1.6 · Cited by 6
XL-Editor: Post-editing Sentences with XLNetPreprint
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Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational AgentsPreprint
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Text style transfer between classical and modern chinese through prompt-based reinforcement learning
Xu, M., Peng, M., & Liu, F. (2022) · World Wide Web · doi:10.1007/s11280-022-01083-6 · Cited by 6
Put Your Voice on Stage: Personalized Headline Generation for News Articles
Ao, X., Luo, L., Wang, X., Yang, Z., Chen, J.-H., Qiao, Y., He, Q., & Xie, X. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3629168 · Cited by 5
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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