Detecting Syntactic Features of Translated Chinese
This addresses a specific challenge in translation studies for Chinese language analysis, though it is incremental as it applies existing machine learning methods to a new linguistic task.
The paper tackled the problem of distinguishing human-translated Chinese texts from originally written Chinese using syntactic features, achieving an F-measure above 90% with constituent parse trees and dependency triples, comparable to lexical n-gram methods.
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.