@inproceedings{kiesel-etal-2023-semeval,
    title = "{S}em{E}val-2023 Task 4: {V}alue{E}val: Identification of Human Values Behind Arguments",
    author = "Kiesel, Johannes  and
      Alshomary, Milad  and
      Mirzakhmedova, Nailia  and
      Heinrich, Maximilian  and
      Handke, Nicolas  and
      Wachsmuth, Henning  and
      Stein, Benno",
    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-1.313",
    doi = "10.18653/v1/2023.semeval-1.313",
    pages = "2287--2303",
    abstract = "Argumentation is ubiquitous in natural language communication, from politics and media to everyday work and private life. Many arguments derive their persuasive power from human values, such as self-directed thought or tolerance, albeit often implicitly. These values are key to understanding the semantics of arguments, as they are generally accepted as justifications for why a particular option is ethically desirable. Can automated systems uncover the values on which an argument draws? To answer this question, 39 teams submitted runs to ValueEval{'}23. Using a multi-sourced dataset of over 9K arguments, the systems achieved F1-scores up to 0.87 (nature) and over 0.70 for three more of 20 universal value categories. However, many challenges remain, as evidenced by the low peak F1-score of 0.39 for stimulation, hedonism, face, and humility.",
}

Argumentation is ubiquitous in natural language communication, from politics and media to everyday work and private life. Many arguments derive their persuasive power from human values, such as self-directed thought or tolerance, albeit often implicitly. These values are key to understanding the semantics of arguments, as they are generally accepted as justifications for why a particular option is ethically desirable. Can automated systems uncover the values on which an argument draws? To answer this question, 39 teams submitted runs to ValueEval{'}23. Using a multi-sourced dataset of over 9K arguments, the systems achieved F1-scores up to 0.87 (nature) and over 0.70 for three more of 20 universal value categories. However, many challenges remain, as evidenced by the low peak F1-score of 0.39 for stimulation, hedonism, face, and humility.
Kiesel, Johannes Alshomary, Milad Mirzakhmedova, Nailia Heinrich, Maximilian H ke, Nicolas Wachsmuth, Henning Stein, Benno
SemEval-2023 Task 4: ValueEval: Identification of Human Values Behind Arguments