# A Baseline Readability Model for Cebuano

arXiv: https://arxiv.org/abs/2203.17225 · HTML: https://mlsanity.com/paper/2203.17225/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 19 references that are in the map.
- First posted on arXiv 31 Mar 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020)
- [Linguistic Features for Readability Assessment](https://mlsanity.com/paper/2006.00377/) (2020)
- [Trends, Limitations and Open Challenges in Automatic Readability Assessment Research](https://mlsanity.com/paper/2105.00973/) (2021)
- [BERT Embeddings for Automatic Readability Assessment](https://mlsanity.com/paper/2106.07935/) (2021)
- [Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature](https://mlsanity.com/paper/2101.10537/) (2021)
- [Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts](https://mlsanity.com/paper/2108.00241/) (2021)

## Built on by (most cited first, 10 of 10)

- [ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability Assessment](https://mlsanity.com/paper/2305.14463/) (2023)
- [LFTK: Handcrafted Features in Computational Linguistics](https://mlsanity.com/paper/2305.15878/) (2023)
- [Automatic Readability Assessment for Closely Related Languages](https://mlsanity.com/paper/2305.13478/) (2023)
- [An Open Multilingual System for Scoring Readability of Wikipedia](https://mlsanity.com/paper/2406.01835/) (2024)
- [Uniform Complexity for Text Generation](https://mlsanity.com/paper/2204.05185/) (2022)
- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2206.05154/) (2022)
- [FilBench: Can LLMs Understand and Generate Filipino?](https://mlsanity.com/paper/2508.03523/) (2025)
- [CebuaNER: A New Baseline Cebuano Named Entity Recognition Model](https://mlsanity.com/paper/2310.00679/) (2023)
- [BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages](https://mlsanity.com/paper/2310.11584/) (2023)
- [Teacher Perception of Automatically Extracted Grammar Concepts for L2 Language Learning](https://mlsanity.com/paper/2310.18417/) (2023)

## Abstract (arXiv)

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.