  Assessing the quality of arguments and of the claims the arguments are
composed of has become a key task in computational argumentation. However, even
if different claims share the same stance on the same topic, their assessment
depends on the prior perception and weighting of the different aspects of the
topic being discussed. This renders it difficult to learn topic-independent
quality indicators. In this paper, we study claim quality assessment
irrespective of discussed aspects by comparing different revisions of the same
claim. We compile a large-scale corpus with over 377k claim revision pairs of
various types from kialo.com, covering diverse topics from politics, ethics,
entertainment, and others. We then propose two tasks: (a) assessing which claim
of a revision pair is better, and (b) ranking all versions of a claim by
quality. Our first experiments with embedding-based logistic regression and
transformer-based neural networks show promising results, suggesting that
learned indicators generalize well across topics. In a detailed error analysis,
we give insights into what quality dimensions of claims can be assessed
reliably. We provide the data and scripts needed to reproduce all results.
