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We present the Touch\'e23-ValueEval Dataset for Identifying Human Valuesbehind Arguments. To investigate approaches for the automated detection ofhuman values behind arguments, we collected 9324 arguments from 6 diversesources, covering religious texts, political discussions, free-text arguments,newspaper editorials, and online democracy platforms. Each argument wasannotated by 3 crowdworkers for 54 values. The Touch\'e23-ValueEval datasetextends the Webis-ArgValues-22. In comparison to the previous dataset, theeffectiveness of a 1-Baseline decreases, but that of an out-of-the-box BERTmodel increases. Therefore, though the classification difficulty increased asper the label distribution, the larger dataset allows for training bettermodels.

The Touch\'e23-ValueEval Dataset for Identifying Human Values behind Arguments