  In this study, we developed the first baseline readability model for the
Cebuano language. Cebuano is the second most-used native language in the
Philippines with about 27.5 million speakers. As the baseline, we extracted
traditional or surface-based features, syllable patterns based from Cebuano's
documented orthography, and neural embeddings from the multilingual BERT model.
Results show that the use of the first two handcrafted linguistic features
obtained the best performance trained on an optimized Random Forest model with
approximately 87% across all metrics. The feature sets and algorithm used also
is similar to previous results in readability assessment for the Filipino
language showing potential of crosslingual application. To encourage more work
for readability assessment in Philippine languages such as Cebuano, we
open-sourced both code and data.
