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

TL;DR

This paper introduces a large-scale dataset and methods for assessing claim quality in argumentation by comparing revisions, enabling topic-independent quality evaluation across diverse domains.

Contribution

It compiles a novel large-scale corpus of claim revisions and proposes new tasks for claim quality assessment independent of discussed aspects.

Findings

Embedding-based models show promising results

Quality indicators generalize across topics

Insights into reliable quality dimensions

Abstract

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.