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
Bibkey:
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)
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PDF:
https://preview.aclanthology.org/author-url/D19-1675.pdf