  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.
