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

Lily Ng, Anne Lauscher, Joel Tetreault, Courtney Napoles


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.
Anthology ID:
2020.argmining-1.13
Volume:
Proceedings of the 7th Workshop on Argument Mining
Month:
December
Year:
2020
Address:
Online
Venues:
ArgMining | COLING
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
117–126
Language:
URL:
https://www.aclweb.org/anthology/2020.argmining-1.13
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.argmining-1.13.pdf