Extracting Implicitly Asserted Propositions in Argumentation

Yohan Jo, Jacky Visser, Chris Reed, Eduard Hovy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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.
Original languageEnglish
Title of host publicationProceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
EditorsBonnie Webber
PublisherAssociation for Computational Linguistics
Pages24-38
Number of pages15
VolumeNovember 2020
DOIs
Publication statusPublished - Nov 2020

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