Intrinsic Quality Assessment of Arguments

Henning Wachsmuth, Till Werner


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.
Anthology ID:
2020.coling-main.592
Volume:
Proceedings of the 28th International Conference on Computational Linguistics
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
6739–6745
Language:
URL:
https://aclanthology.org/2020.coling-main.592
DOI:
10.18653/v1/2020.coling-main.592
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.coling-main.592.pdf