@inproceedings{lee-lee-2023-prompt,
    title = "Prompt-based Learning for Text Readability Assessment",
    author = "Lee, Bruce W.  and
      Lee, Jason",
    editor = "Vlachos, Andreas  and
      Augenstein, Isabelle",
    booktitle = "Findings of the Association for Computational Linguistics: EACL 2023",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.findings-eacl.135/",
    doi = "10.18653/v1/2023.findings-eacl.135",
    pages = "1819--1824",
    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. Our code is available at github.com/brucewlee/prompt-learning-readability."
}