  We propose the novel adaptation of a pre-trained seq2seq model for
readability assessment. We prove that a seq2seq model - T5 or BART - can be
adapted to discern which text is more difficult from two given texts
(pairwise). As an exploratory study to prompt-learn a neural network for text
readability in a text-to-text manner, we report useful tips for future work in
seq2seq training and ranking-based approach to readability assessment.
Specifically, we test nine input-output formats/prefixes and show that they can
significantly influence the final model performance.
  Also, we argue that the combination of text-to-text training and pairwise
ranking setup 1) enables leveraging multiple parallel text simplification data
for teaching readability and 2) trains a neural model for the general concept
of readability (therefore, better cross-domain generalization). At last, we
report a 99.6% pairwise classification accuracy on Newsela and a 98.7% for
OneStopEnglish, through a joint training approach.
