@inproceedings{li-etal-2022-mantis,
    title = "{MANTIS} at {TSAR}-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders",
    author = "Li, Xiaofei  and
      Wiechmann, Daniel  and
      Qiao, Yu  and
      Kerz, Elma",
    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/fix-sig-urls/2022.tsar-1.27/",
    doi = "10.18653/v1/2022.tsar-1.27",
    pages = "243--250",
    abstract = "In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system introduced in Qiang et al. (2020) in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9{\%} accuracy and achieves second place out of 33 ranked solutions."
}