COAST - Customizable Online Syllable Enhancement in Texts. A flexible framework for automatically enhancing reading materials

Heiko Holz, Zarah Weiss, Oliver Brehm, Detmar Meurers


Abstract
This paper presents COAST, a web-based application to easily and automatically enhance syllable structure, word stress, and spacing in texts, that was designed in close collaboration with learning therapists to ensure its practical relevance. Such syllable-enhanced texts are commonly used in learning therapy or private tuition to promote the recognition of syllables in order to improve reading and writing skills. In a state of the art solutions for automatic syllable enhancement, we put special emphasis on syllable stress and support specific marking of the primary syllable stress in words. Core features of our tool are i) a highly customizable text enhancement and template functionality, and ii) a novel crowd-sourcing mechanism that we employ to address the issue of data sparsity in language resources. We successfully tested COAST with real-life practitioners in a series of user tests validating the concept of our framework.
Anthology ID:
W18-0509
Volume:
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venues:
BEA | NAACL | WS
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
89–100
Language:
URL:
https://www.aclweb.org/anthology/W18-0509
DOI:
10.18653/v1/W18-0509
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-0509.pdf