Details
| Original language | English |
|---|---|
| Title of host publication | EACL 2023 |
| Subtitle of host publication | 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023 |
| Pages | 1381-1397 |
| Number of pages | 17 |
| ISBN (electronic) | 9781959429470 |
| Publication status | Published - 2023 |
| Event | 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023 - Dubrovnik, Croatia Duration: 2 May 2023 → 6 May 2023 |
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.
ASJC Scopus subject areas
- Computer Science(all)
- Computational Theory and Mathematics
- Computer Science(all)
- Software
- Social Sciences(all)
- Linguistics and Language
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EACL 2023 : 17th Conference of the European Chapter of the Association for Computational Linguistics, Findings of EACL 2023. 2023. p. 1381-1397.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Topic Ontologies for Arguments
AU - Ajjour, Yamen
AU - Stein, Benno
AU - Kiesel, Johannes
AU - Potthast, Martin
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85159853290&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85159853290
SP - 1381
EP - 1397
BT - EACL 2023
T2 - 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 - Findings of EACL 2023
Y2 - 2 May 2023 through 6 May 2023
ER -