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

Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale