Extracting Implicitly Asserted Propositions in Argumentation
This work addresses a gap in argument mining for computational linguistics, focusing on implicit propositions that are crucial for understanding arguments, though it appears incremental as it applies existing methods to a new aspect of the problem.
The paper tackled the problem of extracting implicitly asserted propositions from rhetorical devices like questions, reported speech, and imperatives in argumentation, which are often overlooked in argument mining systems, and demonstrated the effectiveness and limitations of computational models on a corpus of 2016 U.S. presidential debates and online commentary.
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.