Translation direction refers to whether the translation is done into one’s native language or from one’s native language into the non-native language. The present study aims to employ the probabilistic distribution of sentence and clause lengths to differentiate between translation directions. Specifically, it addresses three research questions: (1) whether sentence length probability distributions can better differentiate between translation directions than average sentence length, (2) whether clause length probability distributions can better differentiate between translation directions than average clause length, and (3) which distributional pattern, rank-frequency (R-F) or length-frequency (L-F), is more sensitive to translation direction. Analysis reveals that mean sentence and clause lengths are unreliable metrics for differentiating between translation directions. In contrast, the parameters of the sentence length L-F distribution, fitted as the Extended Positive Negative Binomial (EPNB) model (k, p), and those of the clause length R-F distribution, fitted as the Hyperpoisson model (a, b), exhibit strong discriminative power for translation direction. The findings demonstrate that probabilistic distributional model can better capture and characterize the nuanced linguistic features of translated texts than simple mean values. The study highlights the effectiveness of the data-driven, probabilistic and quantitative linguistic approach in analyzing the sophisticated translation phenomena.
Zhan et al. (Wed,) studied this question.