Inproceedings,
Conclusion-based Counter-Argument Generation
EACL 2023 - 17th Conference of the European Chapter of the Association for Computational Linguistics, Proceedings of the Conference, page 957--967. Association for Computational Linguistics (ACL), (2023)Funding Information: This work was funded by the Deutsche Forschungs-gemeinschaft (DFG, German Research Foundation): TRR 318/1 2021 - 438445824. We would also like to thank the reviewers and the participants who took part anonymously in our user study.; 17th Conference of the European Chapter of the Association for Computational Linguistics, EACL 2023 ; Conference date: 02-05-2023 Through 06-05-2023.
DOI: 10.48550/arXiv.2301.09911
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.
