Unsupervised stance detection for arguments from consequences

Jonathan Kobbe, Ioana Hulpuș, Heiner Stuckenschmidt


Abstract
Social media platforms have become an essential venue for online deliberation where users discuss arguments, debate, and form opinions. In this paper, we propose an unsupervised method to detect the stance of argumentative claims with respect to a topic. Most related work focuses on topic-specific supervised models that need to be trained for every emergent debate topic. To address this limitation, we propose a topic independent approach that focuses on a frequently encountered class of arguments, specifically, on arguments from consequences. We do this by extracting the effects that claims refer to, and proposing a means for inferring if the effect is a good or bad consequence. Our experiments provide promising results that are comparable to, and in particular regards even outperform BERT. Furthermore, we publish a novel dataset of arguments relating to consequences, annotated with Amazon Mechanical Turk.
Anthology ID:
2020.emnlp-main.4
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
50–60
Language:
URL:
https://aclanthology.org/2020.emnlp-main.4
DOI:
10.18653/v1/2020.emnlp-main.4
Bibkey:
Cite (ACL):
Jonathan Kobbe, Ioana Hulpuș, and Heiner Stuckenschmidt. 2020. Unsupervised stance detection for arguments from consequences. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 50–60, Online. Association for Computational Linguistics.
Cite (Informal):
Unsupervised stance detection for arguments from consequences (Kobbe et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.4.pdf
Video:
 https://slideslive.com/38939246