We present the Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments. To investigate approaches for the automated detection of human values behind arguments, we collected 9324 arguments from 6 diverse sources, covering religious texts, political discussions, free-text arguments, newspaper editorials, and online democracy platforms. Each argument was annotated by 3 crowdworkers for 54 values. The Touch\'e23-ValueEval dataset extends the Webis-ArgValues-22. In comparison to the previous dataset, the effectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERT model increases. Therefore, though the classification difficulty increased as per the label distribution, the larger dataset allows for training better models.
@article{arxiv.2301.13771,
title = {The Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments},
author = {Nailia Mirzakhmedova and Johannes Kiesel and Milad Alshomary and Maximilian Heinrich and Nicolas Handke and Xiaoni Cai and Barriere Valentin and Doratossadat Dastgheib and Omid Ghahroodi and Mohammad Ali Sadraei and Ehsaneddin Asgari and Lea Kawaletz and Henning Wachsmuth and Benno Stein},
journal= {arXiv preprint arXiv:2301.13771},
year = {2023}
}