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
Toward Controlled Generation of TextPreprint
Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., & Xing, E. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.00955 · Cited by 867
Self-citationA Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Sordoni, A., Galley, M., Auli, M., Brockett, C., Ji, Y., Mitchell, M., Nie, J., Gao, J., & Dolan, B. (2015) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/v1/n15-1020 · Cited by 782
Style Transfer from Non-Parallel Text by Cross-AlignmentPreprint
Shen, T., Lei, T., Barzilay, R., & Jaakkola, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09655 · Cited by 634
Unsupervised Neural Machine Translation
Artetxe, M., Labaka, G., Agirre, E., & Cho, K. (2017) · arXiv · doi:10.48550/arxiv.1710.11041 · Cited by 632
Women, Men, and Language: A Sociolinguistic Account of Sex Differences in Language
Coates, J. (1986) · Medical Entomology and Zoology · Cited by 567
Data-Driven Response Generation in Social Media
Ritter, A., Cherry, C. A., & Dolan, W. B. (2011) · Cited by 566
Monroe, B. L., Colaresi, M. P., & Quinn, K. M. (2008) · Political Analysis · doi:10.1093/pan/mpn018 · Cited by 542 · cited 1×
“Style lexicons are extracted using the log-odds ratio informative Dirichlet prior (Monroe et al., 2008), a method that identifies words that are statistically overrepresented in each of the categories.”
MethodStyle Transfer in Text: Exploration and Evaluation
Fu, Z., Tan, X., Peng, N., Zhao, D., & Yan, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11330 · Cited by 468
Language and woman's place: text and commentaries
(2005) · Choice Reviews Online · doi:10.5860/choice.42-4486 · Cited by 367
Meteor 1.3: Automatic Metric for Reliable Optimization and Evaluation of Machine Translation Systems
Denkowski, M., & Lavie, A. (2011) · Cited by 344
Generating Phrasal and Sentential Paraphrases: A Survey of Data-Driven Methods
Madnani, N., & Dorr, B. J. (2010) · Computational Linguistics · doi:10.1162/coli_a_00002 · Cited by 299
Demographic dialectal variation in social media: a case study of African-American English
Blodgett, S. L., Green, L., & O'Connor, B. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1120 · Cited by 291 · cited 3×
“Moreover, prior work has shown that the quality of language identification and POS tagging degrades significantly on African American Vernacular English (Blodgett et al., 2016; Jørgensen et al., 2015); YouTube’s automatic captions have higher error rates for women and speakers from Scotland (Rudinger et al.”
BackgroundFindings of the 2015 Workshop on Statistical Machine Translation
Bojar, O., Chatterjee, R., Federmann, C., Haddow, B., Huck, M., Hokamp, C., Koehn, P., Logacheva, V., Monz, C., Negri, M., et al. (2015) · Conference on Machine Translation (WMT) · doi:10.18653/v1/w15-3001 · Cited by 287
Controlling Politeness in Neural Machine Translation via Side Constraints
Sennrich, R., Haddow, B., & Birch, A. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/n16-1005 · Cited by 273
Computational Sociolinguistics: A Survey
Nguyen, D., Doğruöz, A. S., Rosé, C. P., & de Jong, F. M. G. (2016) · Computational Linguistics · doi:10.1162/coli_a_00258 · Cited by 259
Eckert, P., & McConnell-Ginet, S. (2013) · doi:10.1017/cbo9781139245883 · Cited by 252
Globally Coherent Text Generation with Neural Checklist Models
Kiddon, C., Zettlemoyer, L., & Choi, Y. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1032 · Cited by 228
Learning to Paraphrase for Question Answering
Dong, L., Mallinson, J., Reddy, S., & Lapata, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1091 · Cited by 213
Paraphrasing Revisited with Neural Machine Translation
Mallinson, J., Sennrich, R., & Lapata, M. (2017) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/e17-1083 · Cited by 209
A Persona-Based Neural Conversation ModelPreprint
Li, J., Galley, M., Brockett, C., Spithourakis, G., Gao, J., & Dolan, B. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.06155 · Cited by 148
Social Bias in Elicited Natural Language Inferences
Rudinger, R., May, C., & Durme, B. V. (2017) · doi:10.18653/v1/w17-1609 · Cited by 98
Obfuscating Gender in Social Media Writing
Reddy, S., & Knight, K. K. (2016) · Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) · doi:10.18653/v1/w16-5603 · Cited by 86
The Multilingual Paraphrase Database
Ganitkevitch, J., & Callison-Burch, C. (2014) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/2i4tshqy4xju · Cited by 68
Challenges of studying and processing dialects in social media
Jørgensen, A., Hovy, D., & Søgaard, A. (2015) · Workshop on Noisy User-generated Text (W-NUT) · doi:10.18653/v1/w15-4302 · Cited by 64
Learning Sentential Paraphrases from Bilingual Parallel Corpora for Text-to-Text Generation
Ganitkevitch, J., Callison-Burch, C., Napoles, C., & Durme, B. V. (2011) · Cited by 59
A Study of Style in Machine Translation: Controlling the Formality of Machine Translation Output
Niu, X., Martindale, M. J., & Carpuat, M. J. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1299 · Cited by 56
RtGender: A Corpus for Studying Differential Responses to Gender
Voigt, R., Jurgens, D., Prabhakaran, V., Jurafsky, D., & Tsvetkov, Y. (2018) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/3di79eiki43x · Cited by 52
Self-citationBennett, C. L. (2005) · Interspeech (USB) · doi:10.21437/interspeech.2005-79 · Cited by 52
Learning Paraphrastic Sentence Embeddings from Back-Translated Bitext
Wieting, J., Mallinson, J., & Gimpel, K. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1026 · Cited by 21
Personalized Machine Translation: Preserving Original Author TraitsPreprint
Rabinovich, E., Mirkin, S., Patel, R. N., Specia, L., & Wintner, S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1610.05461 · Cited by 4
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.
Cited by
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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