The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
- verfasst von
- Nailia Mirzakhmedova, Johannes Kiesel, Milad Alshomary, Maximilian Heinrich, Nicolas Handke, Xiaoni Cai, Valentin Barriere, Doratossadat Dastgheib, Omid Ghahroodi, MohammadAli SadraeiJavaheri, Ehsaneddin Asgari, Lea Kawaletz, Henning Wachsmuth, Benno Stein
- 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 Touché23-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 Touché23-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.
- Organisationseinheit(en)
-
Institut für Künstliche Intelligenz
Fachgebiet Maschinelle Sprachverarbeitung
- Typ
- Aufsatz in Konferenzband
- Seiten
- 16121-16134
- Anzahl der Seiten
- 14
- Publikationsdatum
- 01.05.2024
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- Elektronische Version(en)
-
https://aclanthology.org/2024.lrec-main.1402/ (Zugang:
Unbekannt)
