In this work, we focus on sentence splitting, a subfield of text simplification, motivated largely by an unproven idea that if you divide a sentence in pieces, it should become easier to understand. Our primary goal in this paper is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two or three? We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than what we create by trisection.

翻译：本研究聚焦于句子拆分——文本简化领域的一个分支，其主要动机源于一个未经证实的假设：将句子分割成若干片段应能提升理解难度。本文的首要目标是验证这一假设是否成立。具体而言，我们探究句子拆分为两个片段与三个片段是否存在差异。基于亚马逊土耳其机器人平台的实验，我们报告了相关发现。更具体地，我们引入贝叶斯建模框架，结合临床语言学、认知语言学等多维视角下的参数，深入探讨特定拆分方式对可读性的影响程度。贝叶斯建模实验提供了明确证据：将句子一分为二对可读性的提升效果显著优于三分法。