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

2020-10-07 20:27:12
Emily Allaway, Kathleen McKeown
arXiv_CL
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2010.03640

PDF

https://arxiv.org/pdf/2010.03640.pdf

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