@inproceedings{nikita-rajpoot-2022-teampn,
    title = "team{PN} at {TSAR}-2022 Shared Task: Lexical Simplification using Multi-Level and Modular Approach",
    author = "Nikita, Nikita  and
      Rajpoot, Pawan",
    editor = "{\v{S}}tajner, Sanja  and
      Saggion, Horacio  and
      Ferr{\'e}s, Daniel  and
      Shardlow, Matthew  and
      Sheang, Kim Cheng  and
      North, Kai  and
      Zampieri, Marcos  and
      Xu, Wei",
    booktitle = "Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, United Arab Emirates (Virtual)",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.tsar-1.26/",
    doi = "10.18653/v1/2022.tsar-1.26",
    pages = "239--242",
    abstract = "Lexical Simplification is the process of reducing the lexical complexity of a text by replacing difficult words with easier-to-read (or understand) expressions while preserving the original information and meaning. This paper explains the work done by our team {\textquotedblleft}teamPN{\textquotedblright} for the English track of TSAR 2022 Shared Task of Lexical Simplification. We created a modular pipeline which combines transformers based models with traditional NLP methods like paraphrasing and verb sense disambiguation. We created a multi-level and modular pipeline where the target text is treated according to its semantics (Part of Speech Tag). The pipeline is multi-level as we utilize multiple source models to find potential candidates for replacement. It is modular as we can switch the source models and their weighting in the final re-ranking."
}