On Classifying whether Two Texts are on the Same Side of an Argument

Erik Körner, Gregor Wiedemann, Ahmad Dawar Hakimi, Gerhard Heyer, Martin Potthast


Abstract
To ease the difficulty of argument stance classification, the task of same side stance classification (S3C) has been proposed. In contrast to actual stance classification, which requires a substantial amount of domain knowledge to identify whether an argument is in favor or against a certain issue, it is argued that, for S3C, only argument similarity within stances needs to be learned to successfully solve the task. We evaluate several transformer-based approaches on the dataset of the recent S3C shared task, followed by an in-depth evaluation and error analysis of our model and the task’s hypothesis. We show that, although we achieve state-of-the-art results, our model fails to generalize both within as well as across topics and domains when adjusting the sampling strategy of the training and test set to a more adversarial scenario. Our evaluation shows that current state-of-the-art approaches cannot determine same side stance by considering only domain-independent linguistic similarity features, but appear to require domain knowledge and semantic inference, too.
Anthology ID:
2021.emnlp-main.795
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10130–10138
Language:
URL:
https://aclanthology.org/2021.emnlp-main.795
DOI:
10.18653/v1/2021.emnlp-main.795
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.795.pdf
Code
 webis-de/emnlp-21