@inproceedings{fengkai-lee-2025-enhancing,
    title = "Enhancing Readability-Controlled Text Modification with Readability Assessment and Target Span Prediction",
    author = "Fengkai, Liu  and
      Lee, John Sie Yuen",
    editor = "Frermann, Lea  and
      Stevenson, Mark",
    booktitle = "Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.starsem-1.23/",
    doi = "10.18653/v1/2025.starsem-1.23",
    pages = "293--303",
    ISBN = "979-8-89176-340-1",
    abstract = "Readability-controlled text modification aims to rewrite an input text so that it reaches a target level of difficulty. This task is closely related to automatic readability assessment (ARA) since, depending on the difficulty level of the input text, it may need to be simplified or complexified. Most previous research in LLM-based text modification has focused on zero-shot prompting, without further input from ARA or guidance on text spans that most likely require revision. This paper shows that ARA models for texts and sentences, as well as predictions of text spans that should be edited, can enhance performance in readability-controlled text modification."
}