Learning from revisions

Quality assessment of claims in argumentation at scale

verfasst von

Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth

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.

Externe Organisation(en)

Universität Bremen
Universität Paderborn

Typ

Aufsatz in Konferenzband

Seiten

1718-1729

Anzahl der Seiten

12

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Software, Theoretische Informatik und Mathematik, Linguistik und Sprache

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2101.10250 (Zugang: Offen)
https://doi.org/10.18653/v1/2021.eacl-main.147 (Zugang: Offen)