Jacky Visser


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Extracting Implicitly Asserted Propositions in Argumentation
Yohan Jo | Jacky Visser | Chris Reed | Eduard Hovy
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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.


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An Online Annotation Assistant for Argument Schemes
John Lawrence | Jacky Visser | Chris Reed
Proceedings of the 13th Linguistic Annotation Workshop

Understanding the inferential principles underpinning an argument is essential to the proper interpretation and evaluation of persuasive discourse. Argument schemes capture the conventional patterns of reasoning appealed to in persuasion. The empirical study of these patterns relies on the availability of data about the actual use of argumentation in communicative practice. Annotated corpora of argument schemes, however, are scarce, small, and unrepresentative. Aiming to address this issue, we present one step in the development of improved datasets by integrating the Argument Scheme Key – a novel annotation method based on one of the most popular typologies of argument schemes – into the widely used OVA software for argument analysis.

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A Cascade Model for Proposition Extraction in Argumentation
Yohan Jo | Jacky Visser | Chris Reed | Eduard Hovy
Proceedings of the 6th Workshop on Argument Mining

We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction. Propositions are the basic units of an argument and the primary building blocks of most argument mining systems. However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes. In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision. We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates. We show promising performance for some tasks and discuss main challenges in proposition extraction.


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Intertextual Correspondence for Integrating Corpora
Jacky Visser | Rory Duthie | John Lawrence | Chris Reed
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)