@inproceedings{king-etal-2021-unbnlp,
    title = "{UNBNLP} at {S}em{E}val-2021 Task 1: Predicting lexical complexity with masked language models and character-level encoders",
    author = "King, Milton  and
      Hakimi Parizi, Ali  and
      Fakharian, Samin  and
      Cook, Paul",
    editor = "Palmer, Alexis  and
      Schneider, Nathan  and
      Schluter, Natalie  and
      Emerson, Guy  and
      Herbelot, Aurelie  and
      Zhu, Xiaodan",
    booktitle = "Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2021.semeval-1.83/",
    doi = "10.18653/v1/2021.semeval-1.83",
    pages = "650--654",
    abstract = "In this paper, we present three supervised systems for English lexical complexity prediction of single and multiword expressions for SemEval-2021 Task 1. We explore the use of statistical baseline features, masked language models, and character-level encoders to predict the complexity of a target token in context. Our best system combines information from these three sources. The results indicate that information from masked language models and character-level encoders can be combined to improve lexical complexity prediction."
}