@inproceedings{ma-etal-2023-pai,
    title = "{PAI} at {S}em{E}val-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module",
    author = "Ma, Long  and
      Sun, Zeye  and
      Jiang, Jiawei  and
      Li, Xuan",
    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://aclanthology.org/2023.semeval-1.34",
    doi = "10.18653/v1/2023.semeval-1.34",
    pages = "256--261",
    abstract = "The Human Value Detection shared task{\textbackslash}cite{kiesel:2023} aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at {\textbackslash}url{https://www.github.com/diqiuzhuanzhuan/semeval2023}.",
}

The Human Value Detection shared task{\textbackslash}cite{kiesel:2023} aims to classify whether or not the argument draws on a set of 20 value categories, given a textual argument. This is a difficult task as the discrimination of human values behind arguments is often implicit. Moreover, the number of label categories can be up to 20 and the distribution of data is highly imbalanced. To address these issues, we employ a multi-label classification model and utilize a class-balanced loss function. Our system wins 5 first places, 2 second places, and 6 third places out of 20 categories of the Human Value Detection shared task, and our overall average score of 0.54 also places third. The code is publicly available at {\textbackslash}url{https://www.github.com/diqiuzhuanzhuan/semeval2023}.
Ma, Long Sun, Zeye Jiang, Jiawei Li, Xuan
PAI at SemEval-2023 Task 4: A General Multi-label Classification System with Class-balanced Loss Function and Ensemble Module