Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations

• URL: http://arxiv.org/abs/2010.03640v1
• Date: Wed, 7 Oct 2020 20:27:12 GMT
• Title: Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations
• Authors: Emily Allaway and Kathleen McKeown
• Abstract summary: We present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. We also propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations.
• Score: 13.153001795077227
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stance detection is an important component of understanding hidden influences in everyday life. Since there are thousands of potential topics to take a stance on, most with little to no training data, we focus on zero-shot stance detection: classifying stance from no training examples. In this paper, we present a new dataset for zero-shot stance detection that captures a wider range of topics and lexical variation than in previous datasets. Additionally, we propose a new model for stance detection that implicitly captures relationships between topics using generalized topic representations and show that this model improves performance on a number of challenging linguistic phenomena.

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