# Under the Microscope: Interpreting Readability Assessment Models for Filipino

arXiv: https://arxiv.org/abs/2110.00157 · HTML: https://mlsanity.com/paper/2110.00157/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 1 Oct 2021.

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

- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Text Readability Assessment for Second Language Learners](https://mlsanity.com/paper/1906.07580/) (2019)
- [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)
- [Global Model Interpretation via Recursive Partitioning](https://mlsanity.com/paper/1802.04253/) (2018)
- [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, 3 of 3)

- [Automatic Readability Assessment for Closely Related Languages](https://mlsanity.com/paper/2305.13478/) (2023)
- [BasahaCorpus: An Expanded Linguistic Resource for Readability Assessment in Central Philippine Languages](https://mlsanity.com/paper/2310.11584/) (2023)
- [Analyzing Traditional and Neural Approaches to Multilingual Readability Assessment](https://mlsanity.com/paper/2609.10792/) (2026)

## Abstract (arXiv)

Readability assessment is the process of identifying the level of ease or difficulty of a certain piece of text for its intended audience. Approaches have evolved from the use of arithmetic formulas to more complex pattern-recognizing models trained using machine learning algorithms. While using these approaches provide competitive results, limited work is done on analyzing how linguistic variables affect model inference quantitatively. In this work, we dissect machine learning-based readability assessment models in Filipino by performing global and local model interpretation to understand the contributions of varying linguistic features and discuss its implications in the context of the Filipino language. Results show that using a model trained with top features from global interpretation obtained higher performance than the ones using features selected by Spearman correlation. Likewise, we also empirically observed local feature weight boundaries for discriminating reading difficulty at an extremely fine-grained level and their corresponding effects if values are perturbed.