The DOI matches its Crossref record; 40 of the 41 references it cites were found in a registry or an index.
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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Cites
BLEU: a method for automatic evaluation of machine translation
Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2001) · Proceedings of the 40th Annual Meeting on Association for Computational Linguistics - ACL '02 · doi:10.3115/1073083.1073135 · Cited by 21,344
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
Sequence to Sequence Learning with Neural Networks
Sutskever, I., Vinyals, O., & Le, Q. V. (2014) · arXiv · doi:10.48550/arxiv.1409.3215 · Cited by 17,766
Effective Approaches to Attention-based Neural Machine Translation
Luong, T., Pham, H., & Manning, C. D. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1166 · Cited by 8,382
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
Self-citationMoses: open source toolkit for statistical machine translation
Koehn, P., Zens, R., Dyer, C., Bojar, O., Constantin, A., Herbst, E., Hoang, H., Birch, A., Callison-Burch, C., Federico, M., et al. (2007) · Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions - ACL '07 · doi:10.3115/1557769.1557821 · Cited by 4,735 · cited 1×
“Data were tokenized using the Moses tokenizer (Koehn et al., 2007).”
MethodGet To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775
Stereotype Threat and Women's Math Performance
Spencer, S. J., Steele, C. M., & Quinn, D. M. (1999) · Journal of Experimental Social Psychology · doi:10.1006/jesp.1998.1373 · Cited by 3,435
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
Johnson, M., Schuster, M., Le, Q. V., Krikun, M., Wu, Y., Chen, Z., Thorat, N., Viégas, F. B., WATTENBERG, M. P., Corrado, G. S., et al. (2017) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00065 · Cited by 1,941
Vinyals, O., & Le, Q. (2015) · arXiv · doi:10.48550/arxiv.1506.05869 · Cited by 1,400
Research works printed but not linked: 9
- Mikel Artetxe, Gorka Labaka, Eneko Agirre, and Kyunghyun Cho. 2018. Unsupervised neural ma- chine translation. In Proc ICLR.
- Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio. 2015. Neural machine translation by jointly learning to align and translate. In Proc. ICLR.
- Junbo, Zhao, Y. Kim, K. Zhang, A. M. Rush, and Y. LeCun. 2017. Adversarially Regularized Autoen- coders for Generating Discrete Structures. ArXiv e- prints.
- Andrej Karpathy and Li Fei-Fei. 2015. Deep visual- semantic alignments for generating image descrip- tions. In Proc. CVPR, pages 3128-3137.
- Guillaume Lample, Alexis Conneau, Ludovic Denoyer, and Marc'Aurelio Ranzato. 2018. Unsupervised machine translation using monolingual corpora only. In Proc. ICLR.
- J. Li, R. Jia, H. He, and P. Liang. 2018. Delete, Re- trieve, Generate: A Simple Approach to Sentiment and Style Transfer. ArXiv e-prints.
- Dong Nguyen, A. Seza Dogruöz, Carolyn P. Rosé, and Franciska de Jong. 2016. Computational soci- olinguistics: A survey. Computational Linguistics, 42(3):537-593.
- Oriol Vinyals and Quoc Le. 2015. A neural conversa- tional model. In Proc. ICML Deep Learning Work- shop.
- Tsung-Hsien Wen, David Vandyke, Nikola Mrksic, Milica Gasic, Lina M Rojas-Barahona, Pei-Hao Su, Stefan Ultes, and Steve Young. 2017. A network- based end-to-end trainable task-oriented dialogue system. In Proc. EACL.
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Sorted by citations
Chaves, A. P., & Gerosa, M. A. (2021) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2020.1841438 · Cited by 572
Conditional BERT Contextual Augmentation
Wu, X. C., Lv, S., Zang, L., Han, J., & Hu, S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22747-0_7 · Cited by 367
Event Extraction as Machine Reading Comprehension
Liu, J., Chen, Y., Liu, K., Bi, W., & Liu, X. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.128 · Cited by 326
Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout
Tan, H., Yu, L., & Bansal, M. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1268 · Cited by 293
Adversarially Regularized AutoencodersPreprint
Zhao, J., Kim, Y., Zhang, K., Rush, A. M., & LeCun, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.04223 · Cited by 291
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
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