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

2020/10/07 by Emily Allaway, Allaway, Emily, Kathleen McKeown +1 · 10 citations
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2010.03640

openalex publication_date 2020/10/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Stance detection is an important component of understanding hidden influences\nin everyday life. Since there are thousands of potential topics to take a\nstance on, most with little to no training data, we focus on zero-shot stance\ndetection: classifying stance from no training examples. In this paper, we\npresent a new dataset for zero-shot stance detection that captures a wider\nrange of topics and lexical variation than in previous datasets. Additionally,\nwe propose a new model for stance detection that implicitly captures\nrelationships between topics using generalized topic representations and show\nthat this model improves performance on a number of challenging linguistic\nphenomena.\n

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• Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences
• PolitiSky24: U.S. Political Bluesky Dataset with User Stance Labels
• Zero-Shot Conversational Stance Detection: Dataset and Approaches

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