STANCY: Stance Classification Based on Consistency Cues

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, pages 6413–6418, Hong Kong, China, November 3–7, 2019. c⃝2019 Association for Computational Linguistics 6413 STANCY: Stance Classification Based on Consistency Cues Kashyap Popat1, Subhabrata Mukherjee2, Andrew Yates1, Gerhard Weikum1 1Max Planck Institute for Informatics, Saarbr¨ucken, Germany 2Microsoft Research, Redmond, WA, USA 1{kpopat,ayates,weikum}@mpi-inf.mpg.de 2subhabrata.mukherjee@microsoft.com Abstract Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires an- alyzing 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. 1 Introduction There is an abundance of contentious claims on the Web including controversial statements from politicians, biased news reports, rumors, etc. People express their perspectives about these controversial claims through various channels like editorials, blog posts, social media, and discussion forums. To achieve a deeper understanding of these claims, we need to understand users’ perspectives and stance towards the claims. Recent research (FNC-1, 2016; Baly et al., 2018; Chen et al., 2019) has shown stance classification to be a critical step for information credibility and automated fact-checking. Prior Work and Limitations: Prior ap- proaches for stance classification proposed in Somasundaran and Wiebe (2010); Anand et al. (2011); Walker et al. (2012); Hasan and Ng (2013, 2014); Sridhar et al. (2015); Sun et al. (2018) rely on various linguistic features, e.g., n-grams, dependency parse tree, opinion lexicons, and sentiment to determine the stance of perspectives regarding controversial topics. Ferreira and Vla- chos (2016) further incorporate natural language claims and propose a logistic regression model using the lexical and semantic features of claims and perspectives. SemEval tasks (Mohammad et al., 2016; Kochkina et al., 2017) and other approaches (Chen and Ku, 2016; Lukasik et al., 2016; Sobhani et al., 2017) have focused on determining stance only in Tweets. Bar-Haim et al. (2017) propose classifiers based on hand-crafted lexicons to identify important phrases in perspectives and their consistency with the claim to predict the stance However, their model critically relies on manual lexicons and assumes that the important phrases in claims are already identified. Neural-network-based approaches for stance classification learn the claim and perspective representations separately and later combine them with conditional LSTM encoding (Augenstein et al., 2016), attention mechanisms (Du et al., 2017) or memory networks (Mohtarami et al., 2018). Some neural network models also incorporate lexical features (Riedel et al., 2017; Hanselowski et al., 2018). None of these approaches leverage knowledge acquired from massive external corpora. theApproach and Contributions: To overcome limitations of prior works, we present STANCY, a neural network model for stance classification. Given an input pair of a claim and a user’s perspective, …

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