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  3. The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments
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The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments

Mirzakhmedova, N

The Touché23-ValueEval Dataset for Identifying Human Values behind Arguments

Mirzakhmedova, N ; Sharif University of Technology | 2024

191 Viewed
  1. Type of Document: Article
  2. Publisher: 2024
  3. Abstract:
  4. 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 4 780 new arguments, doubling the dataset's size to 9 324 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. © 2024 ELRA Language Resource Association: CC BY-NC 4.0
  5. Keywords:
  6. Corpus (Creation, Annotation, etc.) ; Classification (of information) ; Information retrieval systems ; Text processing ; Data set size ; Document Classification ; Doublings ; Free texts ; Human values ; Large-scales ; Political debates ; Text categorization ; Transformer modeling ; Large datasets
  7. Source: 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings ; 2024 , Pages 16121-16134 ; 978-249381410-4 (ISBN)
  8. URL: https://aclanthology.org/2024.lrec-main.1402