The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting

Tadashi Nomoto


Abstract
In this work, we focus on sentence splitting, a subfield of text simplification, primarily motivated by an unproven idea that if you divide a sentence into pieces, it should become easier to understand. Our primary goal in this paper is to determine 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 when we create simplification by trisection.
Anthology ID:
2022.tsar-1.1
Volume:
Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates (Virtual)
Editors:
Sanja Štajner, Horacio Saggion, Daniel Ferrés, Matthew Shardlow, Kim Cheng Sheang, Kai North, Marcos Zampieri, Wei Xu
Venue:
TSAR
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/2022.tsar-1.1
DOI:
10.18653/v1/2022.tsar-1.1
Bibkey:
Cite (ACL):
Tadashi Nomoto. 2022. The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), pages 1–11, Abu Dhabi, United Arab Emirates (Virtual). Association for Computational Linguistics.
Cite (Informal):
The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting (Nomoto, TSAR 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.tsar-1.1.pdf
Video:
 https://aclanthology.org/2022.tsar-1.1.mp4