Assessing sentence readability for German language learners with broad linguistic modeling or readability formulas: When do linguistic insights make a difference?
Zarah Weiß, Detmar Meurers
Abstract
The paper presents a new state-of-the-art sentence-wise readability assessment model for German L2 readers. We build a linguistically broadly informed machine learning model and compare its performance against four commonly used readability formulas. To understand when the linguistic insights used to inform our model make a difference for readability assessment and when simple readability formulas suffice, we compare their performance based on two common automatic readability assessment tasks: predictive regression and sentence pair ranking. We find that leveraging linguistic insights yields top performances across tasks, but that for the identification of simplified sentences also readability formulas -which are easier to compute and more accessible -can be sufficiently precise. Linguistically informed modeling, however, is the only viable option for high quality outcomes in finegrained prediction tasks.