  Automatic readability assessment (ARA) is the task of evaluating the level of
ease or difficulty of text documents for a target audience. For researchers,
one of the many open problems in the field is to make such models trained for
the task show efficacy even for low-resource languages. In this study, we
propose an alternative way of utilizing the information-rich embeddings of BERT
models with handcrafted linguistic features through a combined method for
readability assessment. Results show that the proposed method outperforms
classical approaches in readability assessment using English and Filipino
datasets, obtaining as high as 12.4% increase in F1 performance. We also show
that the general information encoded in BERT embeddings can be used as a
substitute feature set for low-resource languages like Filipino with limited
semantic and syntactic NLP tools to explicitly extract feature values for the
task.
