Details
| Original language | English |
|---|---|
| Title of host publication | COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference |
| Editors | Donia Scott, Nuria Bel, Chengqing Zong |
| Pages | 6739-6745 |
| Number of pages | 7 |
| ISBN (electronic) | 9781952148279 |
| Publication status | Published - 2020 |
| Externally published | Yes |
| Event | 28th International Conference on Computational Linguistics - Barcelona (Online), Spain Duration: 8 Dec 2020 → 13 Dec 2020 |
Abstract
Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument’s arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument’s text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- Computer Science Applications
- Computer Science(all)
- Computational Theory and Mathematics
Cite this
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COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. ed. / Donia Scott; Nuria Bel; Chengqing Zong. 2020. p. 6739-6745.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Intrinsic Quality Assessment of Arguments
AU - Wachsmuth, Henning
AU - Werner, Till
N1 - Publisher Copyright: © 2020 COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference. All rights reserved.
PY - 2020
Y1 - 2020
N2 - Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument’s arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument’s text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.
AB - Several quality dimensions of natural language arguments have been investigated. Some are likely to be reflected in linguistic features (e.g., an argument’s arrangement), whereas others depend on context (e.g., relevance) or topic knowledge (e.g., acceptability). In this paper, we study the intrinsic computational assessment of 15 dimensions, i.e., only learning from an argument’s text. In systematic experiments with eight feature types on an existing corpus, we observe moderate but significant learning success for most dimensions. Rhetorical quality seems hardest to assess, and subjectivity features turn out strong, although length bias in the corpus impedes full validity. We also find that human assessors differ more clearly to each other than to our approach.
UR - http://www.scopus.com/inward/record.url?scp=85101482308&partnerID=8YFLogxK
U2 - 10.18653/v1/2020.coling-main.592
DO - 10.18653/v1/2020.coling-main.592
M3 - Conference contribution
SP - 6739
EP - 6745
BT - COLING 2020 - 28th International Conference on Computational Linguistics, Proceedings of the Conference
A2 - Scott, Donia
A2 - Bel, Nuria
A2 - Zong, Chengqing
T2 - 28th International Conference on Computational Linguistics
Y2 - 8 December 2020 through 13 December 2020
ER -