  Assessing the proper difficulty levels of reading materials or texts in
general is the first step towards effective comprehension and learning. In this
study, we improve the conventional methodology of automatic readability
assessment by incorporating the Word Mover's Distance (WMD) of ranked texts as
an additional post-processing technique to further ground the difficulty level
given by a model. Results of our experiments on three multilingual datasets in
Filipino, German, and English show that the post-processing technique
outperforms previous vanilla and ranking-based models using SVM.
