@inproceedings{he-zhang-2023-zhegu,
    title = "Zhegu at {S}em{E}val-2023 Task 9: Exponential Penalty Mean Squared Loss for Multilingual Tweet Intimacy Analysis",
    author = "He, Pan  and
      Zhang, Yanru",
    editor = {Ojha, Atul Kr.  and
      Do{\u{g}}ru{\"o}z, A. Seza  and
      Da San Martino, Giovanni  and
      Tayyar Madabushi, Harish  and
      Kumar, Ritesh  and
      Sartori, Elisa},
    booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2023.semeval-1.43/",
    doi = "10.18653/v1/2023.semeval-1.43",
    pages = "318--323",
    abstract = "We present the system description of our team Zhegu in SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. We propose {\textbackslash}textbf{\{}EPM{\}} ({\textbackslash}textbf{\{}E{\}}xponential {\textbackslash}textbf{\{}P{\}}enalty {\textbackslash}textbf{\{}M{\}}ean Squared Loss) for the purpose of enhancing the ability of learning difficult samples during the training process. Meanwhile, we also apply several methods (frozen Tuning {\textbackslash}{\&}amp; contrastive learning based on Language) on the XLM-R multilingual language model for fine-tuning and model ensemble. The results in our experiments provide strong faithful evidence of the effectiveness of our methods. Eventually, we achieved a Pearson score of 0.567 on the test set."
}