Prompt-based Learning for Text Readability Assessment

• URL: http://arxiv.org/abs/2302.13139v2
• Date: Sun, 16 Jun 2024 22:50:50 GMT
• Title: Prompt-based Learning for Text Readability Assessment
• Authors: Bruce W. Lee, Jason Hyung-Jong Lee,
• Abstract summary: We propose the novel adaptation of a pre-trained seq2seq model for readability assessment. We prove that a seq2seq model can be adapted to discern which text is more difficult from two given texts (pairwise)
• Score: 0.4757470449749875
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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