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.
@article{arxiv.1910.06048,
title = {STANCY: Stance Classification Based on Consistency Cues},
author = {Kashyap Popat and Subhabrata Mukherjee and Andrew Yates and Gerhard Weikum},
journal= {arXiv preprint arXiv:1910.06048},
year = {2019}
}