mlsanityData to 7 Oct 2026

Post-editese: an Exacerbated Translationese

arXiv 1907.00900 ↗First posted 1 Jul 2019cs.CLcited by 29

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Abstract (arXiv)

Post-editing (PE) machine translation (MT) is widely used for dissemination because it leads to higher productivity than human translation from scratch (HT). In addition, PE translations are found to be of equal or better quality than HTs. However, most such studies measure quality solely as the number of errors. We conduct a set of computational analyses in which we compare PE against HT on three different datasets that cover five translation directions with measures that address different translation universals and laws of translation: simplification, normalisation and interference. We find out that PEs are simpler and more normalised and have a higher degree of interference from the source language than HTs.

Key facts. Cited by 29 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 3; 2023: 4; 2024: 6; 2025: 5; 2026: 5.

29 papers.

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  1. 2021Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translationcited by 70
  2. 2023Iterative Translation Refinement with Large Language Modelscited by 27
  3. 2021Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMTcited by 21
  4. 2022MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialoguecited by 15
  5. 2022Understanding Translationese in Cross-Lingual Summarizationcited by 14
  6. 2021On the Language Coverage Bias for Neural Machine Translationcited by 12
  7. 2025Lost in Literalism: How Supervised Training Shapes Translationese in LLMscited by 11
  8. 2021The Impact of Post-editing and Machine Translation on Creativity and Reading Experiencecited by 10
  9. 2023Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representationcited by 10
  10. 2024To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translationcited by 10
  11. 2023Translating away Translationese without Parallel Datacited by 5
  12. 2021Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversitycited by 4
17 more
  1. 2021On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translationcited by 4
  2. 2024Gender Bias in Machine Translation and The Era of Large Language Modelscited by 4
  3. 2024Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Datacited by 4
  4. 2024Machine Translation Models are Zero-Shot Detectors of Translation Directioncited by 3
  5. 2025skLEP: A Slovak General Language Understanding Benchmarkcited by 3
  6. 2022Original or Translated? On the Use of Parallel Data for Translation Quality Estimationcited by 2
  7. 2025To MT or not to MT: An eye-tracking study on the reception by Dutch readers of different translation and creativity levelscited by 2
  8. 2025Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTscited by 2
  9. 2024Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translationcited by 1
  10. 2026Improving Term Evaluation in Machine Translation: Variation Matterscited by 1
  11. 2023Trained MT Metrics Learn to Cope with Machine-translated Referencescited by 0
  12. 2024Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?cited by 0
  13. 2025Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children's Literature Translationcited by 0
  14. 2026Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpuscited by 0
  15. 2026Testing the Deliteralization Hypothesis in Human and Machine Translationcited by 0
  16. 2026Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editingcited by 0
  17. 2026An Investigation of Translationese in the Generations of Multilingual Large Language Modelscited by 0