sentence and clause length distribution

The present study aims to employ the probabilistic distribution of sentence and clause lengths to distinguish translation directionality. Specifically, it addresses three research questions: (1) whether sentence length probability distributions can better discriminate translation directions than average sentence length, (2) whether clause length probability distributions can better discriminate 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 distinguishing translation directions. In contrast, the parameters of the sentence length L-F distribution, fitted as the Extended Positive Negative Binomial (EPNB) model (<i>k</i>, <i>p</i>), and those of the clause length R-F distribution, fitted as the Hyperpoisson model (<i>a</i>, <i>b</i>), exhibit strong discriminative power for translation directionality. The findings demonstrate that probabilistic distributional patterns 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.