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

authored by
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.

External Organisation(s)
Technische Universität Darmstadt
Università Commerciale Luigi Bocconi
Paderborn University
Julius Maximilian University of Würzburg
Type
Article
Journal
Transactions of the Association for Computational Linguistics
Volume
10
Pages
1392-1422
No. of pages
31
Publication date
22.12.2022
Publication status
Published
Peer reviewed
Yes
Electronic version(s)
https://doi.org/10.1162/tacl_a_00525 (Access: Open)