Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 1392-1422 |
| Seitenumfang | 31 |
| Fachzeitschrift | Transactions of the Association for Computational Linguistics |
| Jahrgang | 10 |
| Ausgabenummer | 10 |
| Publikationsstatus | Veröffentlicht - 22 Dez. 2022 |
| Extern publiziert | Ja |
Abstract
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in: Transactions of the Association for Computational Linguistics, Jahrgang 10, Nr. 10, 22.12.2022, S. 1392-1422.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation
AU - Lauscher, Anne
AU - Wachsmuth, Henning
AU - Gurevych, Iryna
AU - Glavaš, Goran
N1 - Publisher Copyright: © 2022 Association for Computational Linguistics.
PY - 2022/12/22
Y1 - 2022/12/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85144658074&partnerID=8YFLogxK
U2 - 10.1162/tacl_a_00525
DO - 10.1162/tacl_a_00525
M3 - Article
VL - 10
SP - 1392
EP - 1422
JO - Transactions of the Association for Computational Linguistics
JF - Transactions of the Association for Computational Linguistics
IS - 10
ER -