@inproceedings{north-etal-2024-multils,
    title = "{M}ulti{LS}: An End-to-End Lexical Simplification Framework",
    author = "North, Kai  and
      Ranasinghe, Tharindu  and
      Shardlow, Matthew  and
      Zampieri, Marcos",
    editor = "Shardlow, Matthew  and
      Saggion, Horacio  and
      Alva-Manchego, Fernando  and
      Zampieri, Marcos  and
      North, Kai  and
      {\v{S}}tajner, Sanja  and
      Stodden, Regina",
    booktitle = "Proceedings of the Third Workshop on Text Simplification, Accessibility and Readability (TSAR 2024)",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2024.tsar-1.1/",
    doi = "10.18653/v1/2024.tsar-1.1",
    pages = "1--11",
    abstract = "Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence{'}s original meaning. Several datasets exist for LS and each of them specialize in one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1) lexical complexity prediction (LCP), (2) substitute generation, and (3) substitute ranking for Portuguese."
}