mlsanityData to 7 Oct 2026

Evaluating prose style transfer with the Bible

arXiv 1711.04731 ↗First posted 13 Nov 2017cs.CLcited by 23

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Abstract (arXiv)

In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is highly parallel since many Bible versions are included. Sentences are aligned due to the presence of chapter and verse numbers within all versions of the text. In addition to the corpus, we present the results, as measured by the BLEU and PINC metrics, of several models trained on our data which can serve as baselines for future research. While we present these data as a style transfer corpus, we believe that it is of unmatched quality and may be useful for other natural language tasks as well.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Nov 2017.

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Citing papers per year: 2021: 1; 2022: 6; 2023: 2; 2024: 6; 2025: 4; 2026: 4.

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  1. 2022NEWTS: A Corpus for News Topic-Focused Summarizationcited by 27
  2. 2024Can AI Be as Creative as Humans?cited by 17
  3. 2023A Call for Standardization and Validation of Text Style Transfer Evaluationcited by 13
  4. 2024MultiParaDetox: Extending Text Detoxification with Parallel Data to New Languagescited by 13
  5. 2024Multilingual and Explainable Text Detoxification with Parallel Corporacited by 12
  6. 2022Low-Resource Authorship Style Transfer: Can Non-Famous Authors Be Imitated?cited by 9
  7. 2021From Theories on Styles to their Transfer in Text: Bridging the Gap with a Hierarchical Surveycited by 5
  8. 2022Low Resource Style Transfer via Domain Adaptive Meta Learningcited by 4
  9. 2025LLM in the Loop: Creating the ParaDeHate Dataset for Hate Speech Detoxificationcited by 4
  10. 2024Science Out of Its Ivory Tower: Improving Accessibility with Reinforcement Learningcited by 3
  11. 2022Reproduction and Replication of an Adversarial Stylometry Experimentcited by 2
  12. 2025Evaluating Text Style Transfer: A Nine-Language Benchmark for Text Detoxificationcited by 2
11 more
  1. 2022Extending the Vocabulary of Fictional Languages using Neural Networkscited by 1
  2. 2022A Comprehensive Survey of Natural Language Generation Advances from the Perspective of Digital Deceptioncited by 1
  3. 2023Defending Against Authorship Identification Attackscited by 1
  4. 2024Task-Oriented Paraphrase Analyticscited by 1
  5. 2024Simplifying Scholarly Abstracts for Accessible Digital Librariescited by 1
  6. 2025Improving Text Style Transfer using Masked Diffusion Language Models with Inference-time Scalingcited by 1
  7. 2025Challenging Assumptions in Learning Generic Text Style Embeddingscited by 0
  8. 2026Targum -- A Multilingual New Testament Translation Corpuscited by 0
  9. 2026RT-SFT: Text Style Transfer from Non-Parallel Corpora by Roundtrip Translationcited by 0
  10. 2026Style or Content? Evaluating Style Classifiers with Controlled Content Overlapcited by 0
  11. 2026STEB: Style Text Embedding Benchmarkcited by 0