# Unsupervised Identification of Translationese

arXiv: https://arxiv.org/abs/1609.03205 · HTML: https://mlsanity.com/paper/1609.03205/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2016.

## Built on by (most cited first, 9 of 9)

- [High-Dimension Human Value Representation in Large Language Models](https://mlsanity.com/paper/2404.07900/) (2024)
- [Comparing Feature-Engineering and Feature-Learning Approaches for Multilingual Translationese Classification](https://mlsanity.com/paper/2109.07604/) (2021)
- [Original or Translated? A Causal Analysis of the Impact of Translationese on Machine Translation Performance](https://mlsanity.com/paper/2205.02293/) (2022)
- [PreQuEL: Quality Estimation of Machine Translation Outputs in Advance](https://mlsanity.com/paper/2205.09178/) (2022)
- [Explaining Translationese: why are Neural Classifiers Better and what do they Learn?](https://mlsanity.com/paper/2210.13391/) (2022)
- [Measuring Spurious Correlation in Classification: 'Clever Hans' in Translationese](https://mlsanity.com/paper/2308.13170/) (2023)
- [Towards Tailored Recovery of Lexical Diversity in Literary Machine Translation](https://mlsanity.com/paper/2408.17308/) (2024)
- [Automatic Classification of Human Translation and Machine Translation: A Study from the Perspective of Lexical Diversity](https://mlsanity.com/paper/2105.04616/) (2021)
- [Language Lexicons for Hindi-English Multilingual Text Processing](https://mlsanity.com/paper/2106.15105/) (2021)

## Abstract (arXiv)

Translated texts are distinctively different from original ones, to the extent that supervised text classification methods can distinguish between them with high accuracy. These differences were proven useful for statistical machine translation. However, it has been suggested that the accuracy of translation detection deteriorates when the classifier is evaluated outside the domain it was trained on. We show that this is indeed the case, in a variety of evaluation scenarios. We then show that unsupervised classification is highly accurate on this task. We suggest a method for determining the correct labels of the clustering outcomes, and then use the labels for voting, improving the accuracy even further. Moreover, we suggest a simple method for clustering in the challenging case of mixed-domain datasets, in spite of the dominance of domain-related features over translation-related ones. The result is an effective, fully-unsupervised method for distinguishing between original and translated texts that can be applied to new domains with reasonable accuracy.