@inproceedings{yuan-etal-2021-cambridge,
    title = "{C}ambridge at {S}em{E}val-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction",
    author = "Yuan, Zheng  and
      Tyen, Gladys  and
      Strohmaier, David",
    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/jlcl-multiple-ingestion/2021.semeval-1.74/",
    doi = "10.18653/v1/2021.semeval-1.74",
    pages = "590--597",
    abstract = "This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction. We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, frequency pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1). We further present our analysis of the results and discuss our findings."
}