@inproceedings{ghahroodi-etal-2023-sina,
    title = "Sina at {S}em{E}val-2023 Task 4: A Class-Token Attention-based Model for Human Value Detection",
    author = "Ghahroodi, Omid  and
      Sadraei Javaheri, Mohammad Ali  and
      Dastgheib, Doratossadat  and
      Soleymani Baghshah, Mahdieh  and
      Rohban, Mohammad Hossein  and
      Rabiee, Hamid  and
      Asgari, Ehsaneddin",
    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-1.299",
    doi = "10.18653/v1/2023.semeval-1.299",
    pages = "2164--2167",
    abstract = "The human values expressed in argumentative texts can provide valuable insights into the culture of a society. They can be helpful in various applications such as value-based profiling and ethical analysis. However, one of the first steps in achieving this goal is to detect the category of human value from an argument accurately. This task is challenging due to the lack of data and the need for philosophical inference. It also can be challenging for humans to classify arguments according to their underlying human values. This paper elaborates on our model for the SemEval 2023 Task 4 on human value detection. We propose a class-token attention-based model and evaluate it against baseline models, including finetuned BERT language model and a keyword-based approach.",
}

The human values expressed in argumentative texts can provide valuable insights into the culture of a society. They can be helpful in various applications such as value-based profiling and ethical analysis. However, one of the first steps in achieving this goal is to detect the category of human value from an argument accurately. This task is challenging due to the lack of data and the need for philosophical inference. It also can be challenging for humans to classify arguments according to their underlying human values. This paper elaborates on our model for the SemEval 2023 Task 4 on human value detection. We propose a class-token attention-based model and evaluate it against baseline models, including finetuned BERT language model and a keyword-based approach.
Ghahroodi, Omid Sadraei Javaheri, Mohammad Ali Dastgheib, Doratossadat Soleymani Baghshah, Mahdieh Rohban, Mohammad Hossein Rabiee, Hamid Asgari, Ehsaneddin
Sina at SemEval-2023 Task 4: A Class-Token Attention-based Model for Human Value Detection