  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.

Computation and Language