Intrinsic Quality Assessment of Arguments

verfasst von

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.

Externe Organisation(en)

Universität Paderborn

Typ

Aufsatz in Konferenzband

Seiten

6739-6745

Anzahl der Seiten

7

Publikationsdatum

2020

Publikationsstatus

Veröffentlicht

ASJC Scopus Sachgebiete

Theoretische Informatik, Angewandte Informatik, Theoretische Informatik und Mathematik

Elektronische Version(en)

https://doi.org/10.18653/v1/2020.coling-main.592 (Zugang: Offen)