What is wrong with style transfer for texts?
Not on the map. Why?
Abstract (arXiv)
A number of recent machine learning papers work with an automated style transfer for texts and, counter to intuition, demonstrate that there is no consensus formulation of this NLP task. Different researchers propose different algorithms, datasets and target metrics to address it. This short opinion paper aims to discuss possible formalization of this NLP task in anticipation of a further growing interest to it.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Aug 2018.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021Methods for Detoxification of Texts for the Russian Languagecited by 13
- 2021From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Surveycited by 5
- 2023Post Turing: Mapping the landscape of LLM Evaluationcited by 4
- 2021Rethinking Crowd Sourcing for Semantic Similaritycited by 2
- 2023Post Turing: Mapping the landscape of LLM Evaluation3 Nov 2023
- 2021From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Survey29 Oct 2021
- 2021Rethinking Crowd Sourcing for Semantic Similarity24 Sep 2021
- 2021Methods for Detoxification of Texts for the Russian Language19 May 2021