Conclusion-based Counter-Argument Generation

verfasst von

Milad Alshomary, Henning Wachsmuth

Abstract

In real-world debates, the most common way to counter an argument is to reason against its main point, that is, its conclusion. Existing work on the automatic generation of natural language counter-arguments does not address the relation to the conclusion, possibly because many arguments leave their conclusion implicit. In this paper, we hypothesize that the key to effective counter-argument generation is to explicitly model the argument's conclusion and to enforce that the stance of the generated counter is opposite to that conclusion. In particular, we propose a multitask approach that jointly learns to generate both the conclusion and the counter of an input argument. The approach employs a stance-based ranking component that selects the counter from a diverse set of generated candidates whose stance best opposes the generated conclusion. In both automatic and manual evaluation, we provide evidence that our approach generates more relevant and stance-adhering counters than strong baselines.

Organisationseinheit(en)

Institut für Künstliche Intelligenz

Typ

Aufsatz in Konferenzband

Seiten

957-967

Anzahl der Seiten

11

Publikationsdatum

2023

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Theoretische Informatik und Mathematik, Software, Linguistik und Sprache

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2301.09911 (Zugang: Offen)