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Cited by 142Natural Language Processing TechniquesTopic ModelingAuthorship Attribution and Profiling

“Transforming” Delete, Retrieve, Generate Approach for Controlled Text Style Transfer

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Sudhakar, Akhilesh, Upadhyay, Bhargav, Maheswaran, Arjun·2019·Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
DOI 10.18653/v1/d19-1322
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Abstract

Akhilesh Sudhakar, Bhargav Upadhyay, Arjun Maheswaran. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

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  • Training on heterogenous data annotated with control codes has been shown to improve controlled generation in autoregressive language models.

    “Similar to multitask learning, training on heterogenous data annotated with control codes has been shown to improve controlled generation in autoregressive language models (Keskar et al., 2019; ElSahar et al., 2020; Sudhakar et al., 2019; Li et al., 2020).”

    From TLDR: Extreme Summarization of Scientific Documents (2020) · cites this paper as Sudhakar et al., 2019

    Section 2.3.2 describes an autoregressive transformer whose B-GST input includes a special token for the target style, and Section 4.3 reports improved style-transfer results. But the paper does not show that training on heterogeneous data annotated with control codes caused that improvement; Section 2.3.3 describes reconstruction training for style transfer, not a comparison of heterogeneous control-code training against an alternative. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 18

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  3. Zhenxin Fu, Xiaoye Tan, Nanyun Peng, Dongyan Zhao, and Rui Yan. 2018. Style transfer in text: Exploration and evaluation. In Thirty-Second AAAI Conference on Artificial Intelligence.
  4. Amir Globerson and Sam Roweis. 2006. Nightmare at test time: Robust learning by feature deletion. In Proceedings of the 23rd International Conference on Machine Learning, ICML '06, pages 353-360, New York, NY, USA. ACM. · DOI · Paper page
  5. Zhiting Hu, Zichao Yang, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing. 2017. Toward con- trolled generation of text. In Proceedings of the 34th International Conference on Machine Learning, vol- ume 70 of Proceedings of Machine Learning Re- search, pages 1587-1596, International Convention Centre, Sydney, Australia. PMLR.
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  7. Juncen Li, Robin Jia, He He, and Percy Liang. 2018. Delete, retrieve, generate: a simple approach to sen- timent and style transfer. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1865-1874, New Orleans, Louisiana. Asso- ciation for Computational Linguistics. · DOI
  8. Courtney Napoles, Keisuke Sakaguchi, Matt Post, and Joel Tetreault. 2015. Ground truth for grammati- cal error correction metrics. In Proceedings of the 53rd Annual Meeting of the Association for Compu- tational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol- ume 2: Short Papers), pages 588-593, Beijing, China. Association for Computational Linguistics. · DOI
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  13. Rob Voigt, David Jurgens, Vinodkumar Prabhakaran, Dan Jurafsky, and Yulia Tsvetkov. 2018. RtGender: A corpus for studying differential responses to gen- der. In Proc. LREC.
  14. Jingjing Xu, Xu SUN, Qi Zeng, Xiaodong Zhang, Xu- ancheng Ren, Houfeng Wang, and Wenjie Li. 2018. Unpaired sentiment-to-sentiment translation: A cy- cled reinforcement learning approach. In Proceed- ings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Pa- pers), pages 979-988, Melbourne, Australia. Asso- ciation for Computational Linguistics.
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Cited by

Sorted by citations

Authors
Sudhakar, A., Upadhyay, B., & Maheswaran, A.
Affiliations
  • Indian Institute of Technology BHU (IN)
  • Intel (United States) (US)
ORCID
Upadhyay, Bhargav
Venue
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Publisher
Association for Computational Linguistics
Published
2019
Pages
3267-3277
DOI
10.18653/v1/d19-1322
OpenAlex
W2969646439
Cited by
142
References
4
Citations by year
2026: 2 · 2025: 7 · 2024: 9 · 2023: 27 · 2022: 27 · 2021: 47 · 2020: 23
FWCI
9.28
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
  • Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
Keywords
text style transfer, text generation

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