Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation

verfasst von

Anne Lauscher, Henning Wachsmuth, Iryna Gurevych, Goran Glavaš

Abstract

Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.

Externe Organisation(en)

Technische Universität Darmstadt
Università Commerciale Luigi Bocconi
Universität Paderborn
Julius-Maximilians-Universität Würzburg

Typ

Artikel

Journal

Transactions of the Association for Computational Linguistics

Band

10

Seiten

1392-1422

Anzahl der Seiten

31

Publikationsdatum

22.12.2022

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

Elektronische Version(en)

https://doi.org/10.1162/tacl_a_00525 (Zugang: Offen)