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 (k, p), and those of the clause length R-F distribution, fitted as the Hyperpoisson model (a, b), 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.

本研究旨在借助句子与小句长度的概率分布，甄别翻译方向性（translation directionality）。具体而言，本研究聚焦三个研究问题：其一，相较于平均句子长度，句子长度概率分布能否更有效地判别翻译方向？其二，相较于平均小句长度，小句长度概率分布能否更有效地判别翻译方向？其三，秩频（R-F）与长频（L-F）这两类分布模式中，哪一种对翻译方向性更为敏感？分析结果表明，平均句子长度与平均小句长度作为甄别翻译方向的指标，可靠性不足。与之相对，拟合为扩展正负二项式（EPNB）模型（参数k、p）的句子长度长频（L-F）分布参数，以及拟合为超泊松（Hyperpoisson）模型（参数a、b）的小句长度秩频（R-F）分布参数，均展现出极强的翻译方向判别能力。研究结果证实，相较于简单的均值指标，概率分布模式能够更精准地捕捉并刻画译语文本中细微的语言特征。本研究亦凸显了数据驱动、概率化与量化的语言学研究方法，在解析复杂翻译现象时的有效性。