A Baseline Readability Model for Cebuano

Joseph Marvin Imperial, Lloyd Lois Antonie Reyes, Michael Antonio Ibanez, Ranz Sapinit, Mohammed Hussien


Abstract
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.
Anthology ID:
2022.bea-1.5
Volume:
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Month:
July
Year:
2022
Address:
Seattle, Washington
Venues:
BEA | NAACL
SIG:
SIGEDU
Publisher:
Association for Computational Linguistics
Note:
Pages:
27–32
Language:
URL:
https://aclanthology.org/2022.bea-1.5
DOI:
10.18653/v1/2022.bea-1.5
Bibkey:
Cite (ACL):
Joseph Marvin Imperial, Lloyd Lois Antonie Reyes, Michael Antonio Ibanez, Ranz Sapinit, and Mohammed Hussien. 2022. A Baseline Readability Model for Cebuano. In Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022), pages 27–32, Seattle, Washington. Association for Computational Linguistics.
Cite (Informal):
A Baseline Readability Model for Cebuano (Imperial et al., BEA 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.bea-1.5.pdf
Code
 imperialite/cebuano-readability