# Post-editese: an Exacerbated Translationese

arXiv: https://arxiv.org/abs/1907.00900 · HTML: https://mlsanity.com/paper/1907.00900/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2019.

## Built on by (most cited first, 29 of 29)

- [Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation](https://mlsanity.com/paper/2102.00287/) (2021)
- [Iterative Translation Refinement with Large Language Models](https://mlsanity.com/paper/2306.03856/) (2023)
- [Language Modeling, Lexical Translation, Reordering: The Training Process of NMT through the Lens of Classical SMT](https://mlsanity.com/paper/2109.01396/) (2021)
- [MULTI3NLU++: A Multilingual, Multi-Intent, Multi-Domain Dataset for Natural Language Understanding in Task-Oriented Dialogue](https://mlsanity.com/paper/2212.10455/) (2022)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [On the Language Coverage Bias for Neural Machine Translation](https://mlsanity.com/paper/2106.03297/) (2021)
- [Lost in Literalism: How Supervised Training Shapes Translationese in LLMs](https://mlsanity.com/paper/2503.04369/) (2025)
- [The Impact of Post-editing and Machine Translation on Creativity and Reading Experience](https://mlsanity.com/paper/2101.06125/) (2021)
- [Lost in Translationese? Reducing Translation Effect Using Abstract Meaning Representation](https://mlsanity.com/paper/2304.11501/) (2023)
- [To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation](https://mlsanity.com/paper/2401.01419/) (2024)
- [Translating away Translationese without Parallel Data](https://mlsanity.com/paper/2310.18830/) (2023)
- [Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity](https://mlsanity.com/paper/2105.04616/) (2021)
- [On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation](https://mlsanity.com/paper/2106.15818/) (2021)
- [Gender Bias in Machine Translation and The Era of Large Language Models](https://mlsanity.com/paper/2401.10016/) (2024)
- [Beyond Human-Only: Evaluating Human-Machine Collaboration for Collecting High-Quality Translation Data](https://mlsanity.com/paper/2410.11056/) (2024)
- [Machine Translation Models are Zero-Shot Detectors of Translation Direction](https://mlsanity.com/paper/2401.06769/) (2024)
- [skLEP: A Slovak General Language Understanding Benchmark](https://mlsanity.com/paper/2506.21508/) (2025)
- [Original or Translated? On the Use of Parallel Data for Translation Quality Estimation](https://mlsanity.com/paper/2212.10257/) (2022)
- [To MT or not to MT: An eye-tracking study on the reception by Dutch readers of different translation and creativity levels](https://mlsanity.com/paper/2504.19850/) (2025)
- [Decoding Machine Translationese in English-Chinese News: LLMs vs. NMTs](https://mlsanity.com/paper/2506.22050/) (2025)
- [Multi-perspective Alignment for Increasing Naturalness in Neural Machine Translation](https://mlsanity.com/paper/2412.08473/) (2024)
- [Improving Term Evaluation in Machine Translation: Variation Matters](https://mlsanity.com/paper/2609.08779/) (2026)
- [Trained MT Metrics Learn to Cope with Machine-translated References](https://mlsanity.com/paper/2312.00536/) (2023)
- [Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?](https://mlsanity.com/paper/2409.09568/) (2024)
- [Can Peter Pan Survive MT? A Stylometric Study of LLMs, NMTs, and HTs in Children's Literature Translation](https://mlsanity.com/paper/2506.22038/) (2025)
- [Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus](https://mlsanity.com/paper/2605.00618/) (2026)
- [Testing the Deliteralization Hypothesis in Human and Machine Translation](https://mlsanity.com/paper/2605.25686/) (2026)
- [Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing](https://mlsanity.com/paper/2606.10113/) (2026)
- [An Investigation of Translationese in the Generations of Multilingual Large Language Models](https://mlsanity.com/paper/2608.17399/) (2026)

## 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.