Prompt-based Learning for Text Readability Assessment
Bruce W. Lee, Jason D. Lee · 2023
We propose the novel adaptation of a pretrained 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.Our code is available at github.com/brucewlee/prompt-learning-readability.