Extracting Implicitly Asserted Propositions in Argumentation

Yohan Jo, Jacky Visser, Chris Reed, Eduard Hovy


Abstract
Argumentation accommodates various rhetorical devices, such as questions, reported speech, and imperatives. These rhetorical tools usually assert argumentatively relevant propositions rather implicitly, so understanding their true meaning is key to understanding certain arguments properly. However, most argument mining systems and computational linguistics research have paid little attention to implicitly asserted propositions in argumentation. In this paper, we examine a wide range of computational methods for extracting propositions that are implicitly asserted in questions, reported speech, and imperatives in argumentation. By evaluating the models on a corpus of 2016 U.S. presidential debates and online commentary, we demonstrate the effectiveness and limitations of the computational models. Our study may inform future research on argument mining and the semantics of these rhetorical devices in argumentation.
Anthology ID:
2020.emnlp-main.2
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
24–38
Language:
URL:
https://aclanthology.org/2020.emnlp-main.2
DOI:
10.18653/v1/2020.emnlp-main.2
Bibkey:
Cite (ACL):
Yohan Jo, Jacky Visser, Chris Reed, and Eduard Hovy. 2020. Extracting Implicitly Asserted Propositions in Argumentation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 24–38, Online. Association for Computational Linguistics.
Cite (Informal):
Extracting Implicitly Asserted Propositions in Argumentation (Jo et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.2.pdf
Video:
 https://slideslive.com/38938708
Code
 yohanjo/emnlp20_prop_extr