@inproceedings{mirzakhmedova-etal-2024-touche23,
    title = "The Touch{\'e}23-{V}alue{E}val Dataset for Identifying Human Values behind Arguments",
    author = "Mirzakhmedova, Nailia  and
      Kiesel, Johannes  and
      Alshomary, Milad  and
      Heinrich, Maximilian  and
      Handke, Nicolas  and
      Cai, Xiaoni  and
      Barriere, Valentin  and
      Dastgheib, Doratossadat  and
      Ghahroodi, Omid  and
      SadraeiJavaheri, MohammadAli  and
      Asgari, Ehsaneddin  and
      Kawaletz, Lea  and
      Wachsmuth, Henning  and
      Stein, Benno",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1402",
    pages = "16121--16134",
    abstract = "While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touch{\'e}23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset{'}s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touch{\'e}23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.",
}

While human values play a crucial role in making arguments persuasive, we currently lack the necessary extensive datasets to develop methods for analyzing the values underlying these arguments on a large scale. To address this gap, we present the Touch{\'e}23-ValueEval dataset, an expansion of the Webis-ArgValues-22 dataset. We collected and annotated an additional 4780 new arguments, doubling the dataset{'}s size to 9324 arguments. These arguments were sourced from six diverse sources, covering religious texts, community discussions, free-text arguments, newspaper editorials, and political debates. Each argument is annotated by three crowdworkers for 54 human values, following the methodology established in the original dataset. The Touch{\'e}23-ValueEval dataset was utilized in the SemEval 2023 Task 4. ValueEval: Identification of Human Values behind Arguments, where an ensemble of transformer models demonstrated state-of-the-art performance. Furthermore, our experiments show that a fine-tuned large language model, Llama-2-7B, achieves comparable results.
Mirzakhmedova, Nailia Kiesel, Johannes Alshomary, Milad Heinrich, Maximilian H ke, Nicolas Cai, Xiaoni Barriere, Valentin Dastgheib, Doratossadat Ghahroodi, Omid SadraeiJavaheri, MohammadAli Asgari, Ehsaneddin Kawaletz, Lea Wachsmuth, Henning Stein, Benno
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments