  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.
