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        <title>Topic Ontologies for Arguments</title>
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    <abstract>Many computational argumentation tasks, such as stance classification, are topic-dependent: The effectiveness of approaches to these tasks depends largely on whether they are trained with arguments on the same topics as those on which they are tested. The key question is: What are these training topics? To answer this question, we take the first step of mapping the argumentation landscape with The Argument Ontology (TAO). TAO draws on three authoritative sources for argument topics: the World Economic Forum, Wikipedia’s list of controversial topics, and Debatepedia. By comparing the topics in our ontology with those in 59 argument corpora, we perform the first comprehensive assessment of their topic coverage. While TAO already covers most of the corpus topics, the corpus topics barely cover all the topics in TAO. This points to a new goal for corpus construction to achieve a broad topic coverage and thus better generalizability of computational argumentation approaches.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-eacl.104</identifier>
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%0 Conference Proceedings
%T Topic Ontologies for Arguments
%A Ajjour, Yamen
%A Kiesel, Johannes
%A Stein, Benno
%A Potthast, Martin
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F ajjour-etal-2023-topic
%X Many computational argumentation tasks, such as stance classification, are topic-dependent: The effectiveness of approaches to these tasks depends largely on whether they are trained with arguments on the same topics as those on which they are tested. The key question is: What are these training topics? To answer this question, we take the first step of mapping the argumentation landscape with The Argument Ontology (TAO). TAO draws on three authoritative sources for argument topics: the World Economic Forum, Wikipedia’s list of controversial topics, and Debatepedia. By comparing the topics in our ontology with those in 59 argument corpora, we perform the first comprehensive assessment of their topic coverage. While TAO already covers most of the corpus topics, the corpus topics barely cover all the topics in TAO. This points to a new goal for corpus construction to achieve a broad topic coverage and thus better generalizability of computational argumentation approaches.
%R 10.18653/v1/2023.findings-eacl.104
%U https://aclanthology.org/2023.findings-eacl.104
%U https://doi.org/10.18653/v1/2023.findings-eacl.104
%P 1411-1427

Markdown (Informal)

[Topic Ontologies for Arguments](https://aclanthology.org/2023.findings-eacl.104) (Ajjour et al., Findings 2023)

• Topic Ontologies for Arguments (Ajjour et al., Findings 2023)

ACL

• Yamen Ajjour, Johannes Kiesel, Benno Stein, and Martin Potthast. 2023. Topic Ontologies for Arguments. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1411–1427, Dubrovnik, Croatia. Association for Computational Linguistics.