STANCY: Stance Classification Based on Consistency Cues
Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, Gerhard Weikum
Abstract
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users’ perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines.- Anthology ID:
- D19-1675
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6413–6418
- Language:
- URL:
- https://aclanthology.org/D19-1675
- DOI:
- 10.18653/v1/D19-1675
- Cite (ACL):
- Kashyap Popat, Subhabrata Mukherjee, Andrew Yates, and Gerhard Weikum. 2019. STANCY: Stance Classification Based on Consistency Cues. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6413–6418, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- STANCY: Stance Classification Based on Consistency Cues (Popat et al., EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/nodalida-main-page/D19-1675.pdf