  We report two essential improvements in readability assessment: 1. three
novel features in advanced semantics and 2. the timely evidence that
traditional ML models (e.g. Random Forest, using handcrafted features) can
combine with transformers (e.g. RoBERTa) to augment model performance. First,
we explore suitable transformers and traditional ML models. Then, we extract
255 handcrafted linguistic features using self-developed extraction software.
Finally, we assemble those to create several hybrid models, achieving
state-of-the-art (SOTA) accuracy on popular datasets in readability assessment.
The use of handcrafted features help model performance on smaller datasets.
Notably, our RoBERTA-RF-T1 hybrid achieves the near-perfect classification
accuracy of 99%, a 20.3% increase from the previous SOTA.
