Belief-based Generation of Argumentative Claims

authored by

Milad Alshomary, Wei Fan Chen, Timon Gurcke, Henning Wachsmuth

Abstract

When engaging in argumentative discourse, skilled human debaters tailor claims to the audience's beliefs to construct effective arguments. Recently, the field of computational argumentation witnessed extensive effort to address the automatic generation of arguments. However, existing approaches do not perform any audience-specific adaptation. In this work, we aim to bridge this gap by studying the task of belief-based claim generation: Given a controversial topic and a set of beliefs, generate an argumentative claim tailored to the beliefs. To tackle this task, we model the people's prior beliefs through their stances on controversial topics and extend state-of-the-art text generation models to generate claims conditioned on the beliefs. Our automatic evaluation confirms the ability of our approach to adapt claims to a set of given beliefs. In a manual study, we also evaluate the generated claims in terms of informativeness and their likelihood to be uttered by someone with a respective belief. Our results reveal the limitations of modeling users' beliefs based on their stances. Still, they demonstrate the potential of encoding beliefs into argumentative texts, laying the ground for future exploration of audience reach.

External Organisation(s)

Paderborn University

Type

Conference contribution

Pages

224-233

No. of pages

10

Publication date

04.2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Software, Computational Theory and Mathematics, Linguistics and Language

Electronic version(s)

https://doi.org/10.48550/arXiv.2101.09765 (Access: Open)
https://doi.org/10.18653/v1/2021.eacl-main.17 (Access: Open)