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STANCY: Stance Classification Based on Consistency Cues

MPS-Authors
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Popat,  Kashyap
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

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Fulltext (public)

arXiv:1910.06048.pdf
(Preprint), 421KB

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Citation

Popat, K., Mukherjee, S., Yates, A., & Weikum, G. (2019). STANCY: Stance Classification Based on Consistency Cues. Retrieved from http://arxiv.org/abs/1910.06048.


Cite as: http://hdl.handle.net/21.11116/0000-0005-83E2-7
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.