# Detecting Syntactic Features of Translated Chinese

arXiv: https://arxiv.org/abs/1804.08756 · HTML: https://mlsanity.com/paper/1804.08756/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2018.

## Built on by (most cited first, 2 of 2)

- [The Comparison of Translationese in Machine Translation and Human Transation in terms of Translation Relations](https://mlsanity.com/paper/2404.08661/) (2024)
- [Translationese-index: Using Likelihood Ratios for Graded and Generalizable Measurement of Translationese](https://mlsanity.com/paper/2507.12260/) (2025)

## Abstract (arXiv)

We present a machine learning approach to distinguish texts translated to Chinese (by humans) from texts originally written in Chinese, with a focus on a wide range of syntactic features. Using Support Vector Machines (SVMs) as classifier on a genre-balanced corpus in translation studies of Chinese, we find that constituent parse trees and dependency triples as features without lexical information perform very well on the task, with an F-measure above 90%, close to the results of lexical n-gram features, without the risk of learning topic information rather than translation features. Thus, we claim syntactic features alone can accurately distinguish translated from original Chinese. Translated Chinese exhibits an increased use of determiners, subject position pronouns, NP + 'de' as NP modifiers, multiple NPs or VPs conjoined by a Chinese specific punctuation, among other structures. We also interpret the syntactic features with reference to previous translation studies in Chinese, particularly the usage of pronouns.