Creating a Domain-diverse Corpus for Theory-based Argument Quality Assessment
This work addresses the problem of limited data for researchers in computational argumentation, enabling more diverse corpora, though it is incremental as it builds on existing theory-based approaches.
The authors tackled the lack of annotated data for theory-based argument quality assessment by creating GAQCorpus, a large, domain-diverse annotated corpus, which enables computational models to assess arguments based on theoretical dimensions.
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.