Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics |
| Pages | 1718-1729 |
| Number of pages | 12 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021 - Virtual, Online Duration: 19 Apr 2021 → 23 Apr 2021 |
Abstract
Assessing the quality of arguments and of the claims the arguments are composed of has become a key task in computational argumentation. However, even if different claims share the same stance on the same topic, their assessment depends on the prior perception and weighting of the different aspects of the topic being discussed. This renders it difficult to learn topic-independent quality indicators. In this paper, we study claim quality assessment irrespective of discussed aspects by comparing different revisions of the same claim. We compile a large-scale corpus with over 377k claim revision pairs of various types from kialo.com, covering diverse topics from politics, ethics, entertainment, and others. We then propose two tasks: (a) assessing which claim of a revision pair is better, and (b) ranking all versions of a claim by quality. Our first experiments with embedding-based logistic regression and transformer-based neural networks show promising results, suggesting that learned indicators generalize well across topics. In a detailed error analysis, we give insights into what quality dimensions of claims can be assessed reliably. We provide the data and scripts needed to reproduce all results.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computational Theory and Mathematics
- Social Sciences(all)
- Linguistics and Language
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Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics. 2021. p. 1718-1729.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Learning from revisions
T2 - 16th Conference of the European Chapter of the Associationfor Computational Linguistics, EACL 2021
AU - Skitalinskaya, Gabriella
AU - Klaff, Jonas
AU - Wachsmuth, Henning
N1 - Funding Information: We thank Andreas Breiter for feedback on early drafts, and the anonymous reviewers for their helpful comments. This work was partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under project number 374666841, SFB 1342.
PY - 2021
Y1 - 2021
N2 - Assessing the quality of arguments and of the claims the arguments are composed of has become a key task in computational argumentation. However, even if different claims share the same stance on the same topic, their assessment depends on the prior perception and weighting of the different aspects of the topic being discussed. This renders it difficult to learn topic-independent quality indicators. In this paper, we study claim quality assessment irrespective of discussed aspects by comparing different revisions of the same claim. We compile a large-scale corpus with over 377k claim revision pairs of various types from kialo.com, covering diverse topics from politics, ethics, entertainment, and others. We then propose two tasks: (a) assessing which claim of a revision pair is better, and (b) ranking all versions of a claim by quality. Our first experiments with embedding-based logistic regression and transformer-based neural networks show promising results, suggesting that learned indicators generalize well across topics. In a detailed error analysis, we give insights into what quality dimensions of claims can be assessed reliably. We provide the data and scripts needed to reproduce all results.
AB - Assessing the quality of arguments and of the claims the arguments are composed of has become a key task in computational argumentation. However, even if different claims share the same stance on the same topic, their assessment depends on the prior perception and weighting of the different aspects of the topic being discussed. This renders it difficult to learn topic-independent quality indicators. In this paper, we study claim quality assessment irrespective of discussed aspects by comparing different revisions of the same claim. We compile a large-scale corpus with over 377k claim revision pairs of various types from kialo.com, covering diverse topics from politics, ethics, entertainment, and others. We then propose two tasks: (a) assessing which claim of a revision pair is better, and (b) ranking all versions of a claim by quality. Our first experiments with embedding-based logistic regression and transformer-based neural networks show promising results, suggesting that learned indicators generalize well across topics. In a detailed error analysis, we give insights into what quality dimensions of claims can be assessed reliably. We provide the data and scripts needed to reproduce all results.
UR - http://www.scopus.com/inward/record.url?scp=85107285791&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2101.10250
DO - 10.48550/arXiv.2101.10250
M3 - Conference contribution
AN - SCOPUS:85107285791
SN - 9781954085022
SP - 1718
EP - 1729
BT - Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics
Y2 - 19 April 2021 through 23 April 2021
ER -