Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment

TL;DR

This paper introduces GAQCorpus, a large, diverse annotated dataset for theory-based argument quality assessment, addressing the lack of annotated data and enabling improved computational models of argumentation.

Contribution

It presents the creation and annotation of GAQCorpus, a novel, domain-diverse corpus for theory-based argument quality assessment, with crowdsourcing and guidelines to improve annotation reliability.

Findings

GAQCorpus is the first large, domain-diverse corpus for theory-based AQ.

Annotation guidelines improved reliability of subjective judgments.

The corpus supports future research in computational argument quality assessment.

Abstract

Computational models of argument quality (AQ) have focused primarily on assessing the overall quality or just one specific characteristic of an argument, such as its convincingness or its clarity. However, previous work has claimed that assessment based on theoretical dimensions of argumentation could benefit writers, but developing such models has been limited by the lack of annotated data. In this work, we describe GAQCorpus, the first large, domain-diverse annotated corpus of theory-based AQ. We discuss how we designed the annotation task to reliably collect a large number of judgments with crowdsourcing, formulating theory-based guidelines that helped make subjective judgments of AQ more objective. We demonstrate how to identify arguments and adapt the annotation task for three diverse domains. Our work will inform research on theory-based argumentation annotation and enable the creation of more diverse corpora to support computational AQ assessment.