Nomoto Tadashi
National Institute of Japanese Literature, Tokyo, Japan.
Front Artif Intell. 2023 Sep 18;6:1208451. doi: 10.3389/frai.2023.1208451. eCollection 2023.
In this study, 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 study is to find out whether this is true. In particular, we ask, does it matter whether we break a sentence into two, three, or more? 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 when we create simplification with more splits.
在本研究中，我们聚焦于句子拆分，这是文本简化的一个子领域，其主要动机源于一个未经证实的观点，即如果你把一个句子拆分成几个部分，它应该会变得更容易理解。我们在本研究中的主要目标是弄清楚这是否属实。具体而言，我们要问，将句子拆分成两个、三个还是更多部分有关系吗？我们报告基于亚马逊土耳其机器人（Amazon Mechanical Turk）的研究结果。更具体地说，我们引入了一个贝叶斯建模框架，以进一步研究将复杂句子拆分成特定方式在何种程度上会影响可读性，同时还考虑了从包括临床语言学和认知语言学等不同角度采用的一些其他参数。贝叶斯建模实验提供了明确的证据，表明将句子一分为二比拆分成更多部分进行简化时，在更大程度上提高了可读性。
