Prompt-based Learning for Text Readability Assessment
This work addresses readability assessment for text simplification and education, but it is incremental as it adapts existing models to a new task.
The paper tackled text readability assessment by adapting pre-trained seq2seq models like T5 or BART to pairwise ranking, achieving 99.6% accuracy on Newsela and 98.7% on OneStopEnglish datasets.
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.